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It-Jim takes part in Prof2IT project of Kharkiv IT Cluster

Posted on December 18, 2020 by admin

We love teaching. That is why we hold internships and schools, organize applied computer vision meetups, and deliver open lectures at universities. Until autumn 2020, our audience mainly included students, interns, and developers, but now it also has Kharkiv university teachers on the list. Being invited by Kharkiv IT cluster to deliver a course on computer vision and machine learning for lecturers of IT specialties working at Kharkiv universities and colleges who seek to improve their qualifications, we joined the Prof2IT project and contributed to educators’ professional growth.

What is Prof2IT?

The Prof2IT project, held jointly by Kharkiv IT Cluster and Kharkiv University of Technology “Step”, aims at helping Kharkiv university teachers of IT specialties improve their skills and update their knowledge on specific IT and data science disciplines. Additionally, it is targeted to update the course materials they teach in accordance with the modern requirements of the IT industry.

With this project brought to life, Kharkiv IT Cluster contributed to direct communication between the IT industry and the teaching community. IT businesses are interested in ensuring that people who graduate from universities and come to work for them are highly competent and the market is saturated with highly-qualified young talents. Is it always the case? Unfortunately, no. How can we help as a company? We can train a teacher, and let the geometric progression of spreading knowledge do the rest.

Course on Computer Vision and Machine Learning

Our online training course “Applied Computer Vision: from Classic Image Processing to Machine Learning and Deep Learning” started on November 5, 2020, and lasted for two months. Twenty lecturers from ten different Kharkiv educational institutions including but not limited to Kharkiv National Polytechnic University “NTU KhPI”, Kharkiv National University of Radioelectronics and V. N. Karazin Kharkiv National University joined the course. All the materials were delivered by our experienced team:

What did the course include? Seven lectures divided into two modules and 2 workshops that covered all the basics both in classical computer vision algorithms, machine learning, and deep learning. The listeners could update their knowledge on the theoretical foundations of the formation of digital images and their analysis using different tools.

In the first part, we have covered some basics in image understanding and processing like color spaces, histograms, filtering, image gradients, thresholding, and morphological operations. The classical CV module also included feature extraction and matching; lecture on detection and tracking which included an overview of homography, RANSAC, camera calibration, optical flow and template-based tracking, and finally lecture on feature crafting, the bridge between classical approaches and machine learning.

The second module featured an introduction to both classical machine learning algorithms and neural networks. We covered the very basics of deep learning (neuron model, backpropagation, gradient descent, etc.) as well as the evolution of neural network architectures, different types of convolutional layers and regularization techniques, GANs, autoencoders and different task-specific architectures of neural networks.

With this course, we hope to contribute not just to the professional development of the teachers who attended the course but also to the modernization of the corresponding training courses in universities and colleges of the city.

International Consumer Electronics Show – 2021

Posted on December 18, 2020 by admin

For more than 50 years, CES has been the global stage for innovation. And the all-digital CES 2021 will continue to be a platform to launch products, engage with global brands and define the future of the tech industry.

An all-digital CES 2021 will allow the entire tech community to safely share ideas and introduce the products that will shape our future.

We’re happy to participate in all the awe-inspiring moments of CES and see what experience for the tech industry they have to offer in 2021.

Winter Internship @ It-Jim

Posted on December 14, 2020 by admin

A winter internship on computer vision is coming!

February 1-28, 2021: one intense month of solving practical CV tasks under the supervision of It-Jim specialists
Full-time engagement
Bonus: possible employment after successful graduation

If you:

✔️have confident knowledge of linear algebra and C++/Python

✔️have pre-intermediate or higher English level

✔️want to boost skills in CV/ML,

fill in this registration form until January 10, 2021.

Winter Internship on Computer Vision: Time to Become a CV Engineer

Posted on December 14, 2020 by admin

While 2020 has shown us that new year resolutions might not work, we do not think anyone should give up making a clear vision for the upcoming year. Especially if there is a “become a computer vision engineer” on the list.

Nope, we can not tell for sure what 2021 will be like. Yet, being optimists, we promise you this: the start of the next year can be full of discoveries, exploration and even have elements of intrigue. All those things combine when it comes to research in computer vision, and with our winter internship, we are offering you to discover this exciting field for yourself.

Starting February 1, 2021, and for full 4 weeks, you are invited to work on a real computer vision project under the supervision of It-Jim experts. This is a full-time engagement and quite an intense month of diving into the CV/ML/DL world.

What exactly does internship mean?

You might have heard about our summer internships before. Unlike those activities, winter internship-2021 will not have lectures nor workshops. Instead, it will bring you a full immersion into an exciting research project with lots of experiments, deep analysis of algorithms, and their implementation. What is more, you will be guided by experienced It-Jim engineers and learn the practices from the best.

At the end of the internship, you will be asked to make a presentation to showcase your achievements throughout the month. Our vibrant team is always open to bright engineers… What if this could be the start of your career?

How do you know if this is for you?

First, ask yourself if you are eager to dive into the world of visual intelligence. Because once you start, there is no turning back – it is THAT interesting! You know how they say: once a computer vision engineer, always a computer vision engineer 😉

We do not ask much. With a confident knowledge in linear algebra and C++/Python, preintermediate+ level of English, and strong motivation to learn fast, your chances are high. Some analytical and problem-solving skills would not hurt, too.

Are you the one we are looking for? Do you want to enter the world of computer vision? We will provide you with one of the best possible platforms in Ukraine to do that.

Fill in this form: https://forms.gle/6gtvzFBurnXNGsAG7, and let’s find out. We are accepting applications until January 10, 2021. To become an intern, you will need to solve a couple of basic tasks. They will be sent out after the application submission deadline. You will have two weeks to show us what you got.

It-Jim’s winter-2021 internship in a nutshell

Should you have any questions, please contact Daryna Pesina, COO of It-Jim, at darynapesina@it-jim.com.

4 Ways How Computer Vision Is Deepening the Fashion Industry

Posted on December 8, 2020 by admin

What is your first thought when you hear about computer vision (CV) in fashion? Or, what is the first thing that pops into your head when you hear about deep learning fashion? Let us guess – online clothing shopping or virtual try-on applications?

Well, this might be surprising but deep fashion is not a far future anymore. What’s more, fashionably speaking, the usage of deep learning in the fashion industry seems to be already old-fashioned rather than pioneering or innovative. Many famous brands like Dior, Macy’s, Nike, Zara are already using artificial intelligence (AI) in e-commerce, and this is not only about market segments for retail clothing. There is far more than this within intelligent fashion. Most crucially, fashion is all about visuals. And where there are visuals, there is computer vision.

Let’s see how exactly data analytics and AI approaches entered the fashion industry and what happens when so seemingly different fields come together.

As mentioned above, AI-powered tools have been already deeply embedded in many creative fields such as art, film, music, graphic design, advertising, and fashion. Being a multibillion-dollar global industry, fashion is what creates, sets, and sells style and image, and quite often dictates canons of beauty.

Technically, making fashion truly intelligent is a very difficult task due to a huge variability of fashion items in style and design. Current trends on intelligent fashion are aimed at the tasks not only to detect clothing in an image but also analyze and synthesize new ones, and, hence, offer tailored recommendations. Within deep learning in the fashion industry, three main aspects appear definable: low-level pixel computation, mid-level fashion understanding, and high-level fashion analysis. The former is intended to label certain items on a picture and deals with human and cloth segmentation, landmark detection, and human pose estimation. Mid-level tasks aim to distinguish fashion images like items and styles. And finally, high-level analysis is recommendation-oriented, it includes synthesis and fashion trend forecast.

Here are the use-cases of how CV and deep learning are deepening fashion.

Try-before-You-Buy Solutions

An excellent example of CV-enabled fashion technologies is virtual fitting room applications. These allow potential customers to try on a garment or accessory using various software applications. You must admit it is great! Whether you choose glasses, watches, or hats, you are able to try on a model in real-time easily changing its color and shape.

Gap Dressing Room AR APP By Avametric – source

Such solutions are based on the pose estimation models used for landmarks detection. The deep fashion datasets might be taken from open-source libraries.

A yet harder task is to implement virtual try-on clothes. Because clothing alters its form when taking the shape of a person’s body, for proper augmented reality (AR) experience, a deep learning model should identify not only basic key points on the body’s joints but also the three-dimensional body shape.

Fashion Item Retrieval

Another benefit of using deep learning-based models is the fashion image retrieval task. For some, shopping can be enjoyable things to do, for others, could be absolutely frustrating. If you are not a shopping fan, when buying online can be even more challenging. You are just scrolling and scrolling, browsing gazillions of items, and could nohow find what you’re looking for. Or another case, imagine you saw a gorgeous Jennifer Lopez’s dress/purse/waistband (underline whichever is appropriate :)) and took fire to find something like that. Although many online retailer websites support keyword-based searches, it would be much handier if a mechanism existed which could help us to find the desired apparel based on a visual query rather than a text description alone.

The great news is that CV may perfectly cope with this issue by finding a similar or alternative product you requested. And, most importantly, much faster than you would be searching by yourself. Still, clothing retrieval tasks based on queries by the customer’s picture is highly challenging. This is due to a significant discrepancy between the real-world photos and those captured by retailers. Another problem is that clothing items are highly deformable, and, thus, their appearance may differ dramatically.

To solve the clothing retrieval task there is a trend to create attribute-aware deep neural network architectures that may include both semantic attributes and visual similarity constraints into the feature learning stage. Some of them may exploit over-segmentation algorithms with human pose estimation to get query clothing items and to retrieve similar images from the existing galleries.

And here you can bring up a question: how does CV “know” what exactly should be retrieved?

How Computer Vision Understands Fashion

We know that CV systems are trained to “look” at the picture and generate a list of features for each detected item. This is mainly accomplished by such technology as landmark detection. The fashion landmark detection means recognition of clothing in an image and categorization of fashion items. Fashion landmarks are to define the precise location of such functional clothing regions as a neckline, hemline, sleeves and cuff. However, detecting fashion landmarks is a challenging task due to such constraints as background noise, human poses, and scales. For achieving more accurate landmark prediction, CV algorithms should be more context-aware. Besides that the landmarks indicate the key points on clothes, they also capture their bounding boxes, which helps better discriminate the design, pattern, and class of apparel.

An example of fashion landmark detection – source

To be able to solve the above-mentioned tasks a number of clothes datasets come to the aid. One of the most widespread of them is Deepfashion2. It is a large-scale benchmark with comprehensive tasks and annotations, created by researchers from the Chinese University of Hong Kong. The dataset includes over 800K labeled into categories images with comprehensive descriptive attributes, bounding boxes, and clothing landmarks.

The Big Bang Theory series frame exemplifies the use-case of DeepFashion2 – source

DeepFashion2 allows performing a wide spectrum of tasks such as clothes detection, pose estimation, human and clothes segmentation, and clothing retrieval.

Fashion Recommendation with AI

A popular application of AI in clothing fashion is the deep learning recommendation engine. For e-commerce, it is all about categorization fashion items, clothing analysis, and help in certain style matching. Recommendation for fitting works on the concept of visual compatibility, which performs how favorable different fashion and apparel units can be matched to create a fashionable look. Also, it refers to the personalized recommendations considering such factors as preferable color, print, fabric, and outfit style. And since fashion is not only about what people are wearing but also reveals personality, fashion recommendation technology could help not only in certain cloth matching but in makeup or hairstyle suggestions. In other words, a customer benefits from the intelligent fashion-image consultant.

Another deep fashion application is a virtual assistant or chatbot. This kind of AI-powered software solution is an important part of business communication. Being an effective tool in user request analysis, it responds instantly and assists in keeping in touch with a customer throughout the whole purchase cycle.

Fashion Trends Forecasting Using Deep Learning

Given the frenetic pace in refreshment of fashion and design, retail businesses need to consistently keep up within the forefront and predict consumer preferences for the next season. Traditionally, such estimates are made based on the data from previous years. However, AI-based methods can reduce forecasting errors significantly.

Besides obvious business interests in sales forecasting for the retail clothing market, it is also important for consumers to choose appropriate fashion goods. Deep learning models for fashion are impressively helpful in analyzing current trends and customers’ behavior. So, knowing what is and what expected to be on-trend, businesses can deliver a better brand experience and, thus, provide exactly what shoppers look for.

To sum up, today, AI methods provide multiple solutions for fashion making it more and more intelligent. CV-based deep fashion technologies come into use to handle diverse challenges, such as fashion image detection, item retrieval, analysis and synthesis, recommendation, and popularity prediction.

Computer Vision in Healthcare: Benefits & Key Applications

Posted on November 24, 2020 by admin

Computer Vision in Healthcare: Benefits, Challenges, and Use Cases

Are you interested in using new technologies such as AI and computer vision in healthcare?

Artificial intelligence (AI) and machine learning (ML) are being increasingly used across various sectors, with healthcare being one of them. Computer vision (CV) technology is another powerful tool that helps recognize, interpret, and process visual data.

Computer vision in healthcare can transform existing patient care services by interpreting medical images and assisting in diagnostics with top accuracy. The potential applications of computer vision in the medical field are numerous, ranging from medical diagnostics and patient monitoring to treatment planning and automated health record management.

In this article, we examine the advantages of utilizing computer vision applications in healthcare and discover:

Understand computer vision in healthcare and how it works.
Reasons why computer vision matters in healthcare.
Ethical considerations in computer vision healthcare solutions.
Recent advancements in computer vision for healthcare applications.
Existing challenges in utilizing computer vision in healthcare.
Future scope and trends of computer vision in healthcare.

Let’s start by exploring what computer vision is and its impact on the medical field.

What Is Computer Vision and How Does It Work?

Computer vision, a subset of artificial intelligence (AI), empowers machines to interpret and understand visual data from the world around them. This technology aims to replicate human vision, enabling computers to perceive and process images and videos.

At its core, computer vision engineering focuses on key techniques like image recognition, object detection, and segmentation. These techniques help machines spot and categorize objects in an image, detect their boundaries, and segment different regions.

Computer algorithms learn from vast datasets, improving their accuracy and efficiency in tasks such as disease detection and medical image analysis. The use of a neural network further enhances these processes.

Why Does Computer Vision Matter in Healthcare

Experts cannot overstate the importance of computer vision in healthcare. The healthcare industry is embracing digital transformation, and integrating computer vision technology becomes essential.

AI-powered diagnostics enable healthcare providers to achieve unprecedented levels of precision, speed, and administrative relief. This allows not only to enhance efficiency but also to improve patient care and safety.

Global Market Insights estimated the global market for AI in computer vision at $14.1 billion. Analysts project that the market will grow at a 19.5% CAGR to $82.8 billion by 2034. This exponential growth reflects its critical role in the industry.

The need for more accurate and quicker analysis and healthcare operations drives this upward trend. Computer vision and AI systems are evolving and can now handle complex and varied visual data. This makes them a valuable tool in healthcare.

Additionally, computer vision techniques are revolutionizing the way researchers conduct medical image analysis. AI-driven systems analyze visual data with speed and precision. In contrast, traditional manual analysis is time-consuming and prone to errors. This not only enhances the diagnostic process but also frees up healthcare professionals to focus on direct patient care.

In addition to diagnostics, computer vision technology plays a crucial role in ensuring patient safety and quality of care. Computer vision enables automated remote monitoring systems that track patient conditions in real-time, as well as advanced computer-aided diagnosis, which aids in the early detection of diseases.

Medical professionals can now use computer vision to analyze complex images such as X-rays, MRIs, and CT scans. As a result, diagnostic errors are less likely to occur. This technology also helps ensure timely surgical interventions.

By implementing these innovations, healthcare organizations can improve patient monitoring, streamline workflows, and enhance the quality of care provided.

The benefits of implementing computer vision in healthcare include:

Automate workflows, optimize resources, and speed up administrative tasks in a healthcare organization.
Deliver surgical assistance and improve precision and treatment outcomes.
Help detect anomalies in medical images and minimize diagnostic errors.
Enable continuous monitoring of patients (fall detection, movement tracking, condition changes).
Focus on patient care by handling routine tasks efficiently.
Interpret medical images accurately and effectively.
Enhance the user experience in healthcare environments.

Ethical Considerations in Computer Vision Healthcare Solutions

Ethical and privacy considerations play a crucial role in the implementation of computer vision in healthcare. Ethical considerations encompass data privacy and security, AI bias, and clinical validation.

Healthcare computer vision handles sensitive personal data, meaning it must meet industry standards and certifications. In a specific project case, consider these certifications and standards: FHIR, HIPAA, HITECH, CEHRT, ONC-ATCB, and GDPR.

EHR (electronic health records) certification builds trust among medical professionals, software developers, and patients. It ensures that the system uses data securely and adequately. EHR certification happens after a special executive board evaluates the software. For instance, in the USA, ONC and HHS are two organizations that handle this procedure.

Thus, to receive EHR certification, it is necessary to meet quality standards, mitigate the risks of unauthorized data access or hacking, establish robust data security protocols, and ensure top-notch data encryption and anonymization.

Transparency and accountability are also crucial for designing computer vision applications. Healthcare organizations must adopt transparent practices in data collection, processing, and training computer vision algorithms to ensure accurate and reliable results. Using practices that reduce bias in the training phase of deep learning models is essential for ensuring fairness and accuracy in AI-based applications.

To keep computer vision in healthcare private and secure, follow these criteria:

Robust software infrastructure with advanced security protocols and encryption.
Use of isolated servers, networks, and private cloud environments.
Centralized access control with unified authentication and zero-trust policies.
Autonomous systems that operate without human oversight.
On-device image processing that avoids transmitting or storing data in the cloud.
Local and real-time deep learning use (edge AI).
Transparent data handling and an easily understandable system architecture.

Fairness, transparency, and data security foster trust and encourage ethical use of computer vision technology in healthcare for everyone.

Key Applications of Computer Vision in Healthcare

The potential applications of computer vision in the medical field are multifaceted, ranging from image processing and predictive analysis to automated health record management. This improves the quality of medical services and the healthcare administration system.

Let’s consider computer vision use cases in healthcare today.

1. Computer Vision in Medical Imaging

Currently, the most widespread use of computer vision is image recognition and classification for medical purposes. AI-powered medical imaging can detect abnormalities in X-rays, MRIs, and CT scans faster than traditional analysis methods. Aided by CV and deep learning tools, physicians can inspect and interpret images in-depth, improving the accuracy of diagnosis and adjusting therapy accordingly.

Thus, medical image classification using a convolutional neural network (CNN) is employed to aid in disease diagnosis and treatment. Dealing with MRIs, for example, various CNN architectures may reveal tumors or aneurysms in the brain, and even predict the development of Alzheimer’s disease in the early stages.

Another way to use CV in medical imaging is through facial recognition and video stream processing. One of the benefits is that deep learning algorithms can be successfully trained to reveal even the slightest abnormalities. This aspect could be extremely helpful for patients suspected of developing conditions and rare genetic malfunctions that are difficult to detect in routine screenings.

Several computer vision healthcare companies have developed AI face-scanning applications. Based on ML algorithms and neural networks, they classify distinctive features in photos of patients with congenital and neurodevelopmental disorders.

2. Computer Vision for Surgical Assistance

Computer vision systems also have a great application in surgery. In this field, computer vision is a powerful tool for enhancing surgeon performance by measuring activity levels, detecting chaotic movements, and assessing working times in particular areas – ROI (regions of interest).

The technology enables training and simulation, as well as assessing surgical skills to enhance surgeon performance. This way, medical personnel can effectively prepare for invasive surgical procedures while minimizing potential complications.

Additionally, computer-aided models can accurately reconstruct surfaces and design implants for orthopedic procedures. This CV application can provide prompt and precise segmentation of bones, joints, or soft body tissues. This helps achieve higher levels of accuracy in modeling skeletons and implants for surgeries, taking into account the MRI and CT scans.

Another notable example of a deep learning system for surgical assistance is the Triton project. The system estimates real-time blood loss during and after surgery by visually analyzing blood-soaked sponges, suction containers, and other surgical tools. This tool helps determine the appropriate amount of blood to transfuse during or after the procedure.

3. Computer Vision in Early Disease Detection

Early disease detection is crucial for enhancing patient survival rates and simplifying their treatment. AI-powered systems are trained on a large volume of medical imaging data, allowing for the identification of patterns and anomalies that may not be visible to the human eye. This capability is particularly significant for conditions like lung cancer, where early detection can lead to more effective treatment and a more favorable prognosis.

Additionally, mobile devices equipped with convolutional neural networks can facilitate early diagnosis in dermatology, allowing patients to monitor their skin health more easily. This feature aids in skin cancer detection by analyzing images of skin lesions, enabling timely intervention and reducing the risk of skin cancer progression.

Similarly, AI-driven screening tools enhance preventive care by identifying patients at high risk. These tools are handy to diagnose diseases such as Alzheimer’s disease, cardiovascular disorders, and diabetic retinopathy.

4. Tumor and Cancer Detection with CV

One of the most significant applications of computer vision in healthcare is the detection and segmentation of tumors. Deep learning technologies have significantly enhanced the accuracy of detecting those, allowing for earlier cancer diagnosis and treatment.

Segmentation techniques such as Mask R-CNN are used to provide accurate and detailed outlines of tumors or melanomas. This automation streamlines the detection process, making it less time-consuming and tedious, and allowing radiologists to focus on their vital tasks.

Moreover, deep learning models have achieved physician-level accuracy in cancer detection, highlighting the potential of computer vision to augment human expertise. For instance, a recent study on breast cancer reveals the successful application of AI and deep learning methods, achieving a model accuracy of 97.18%.

Another key aspect of tumor detection is characterizing tumors based on their morphologically relevant features, such as roundness and aspect ratio. These features help in analyzing the shape and structure of tumors, leading to more accurate diagnoses and personalized treatment plans.

5. Automated Health Monitoring with Computer Vision

Another example of a successful CV application is the real-time tracking of vital signs and fitness characteristics. This application can prevent acute neurological and cardiac events, such as strokes and heart attacks.

Computer vision has shown promise in remote patient monitoring, enhancing care for chronic conditions and post-surgery, particularly among elderly individuals. Additionally, by utilizing AI-driven technology, personnel can make clinical decisions more quickly for emergency care prioritization and optimal timing for surgeries.

6. Infection Prevention & Control of Pandemics

Artificial intelligence and deep learning solutions can become a valuable method for controlling and preventing pandemics.

For instance, the open-source community COVID-Net was the first to develop a convolutional neural network for detecting coronavirus cases from CXR images. It is possible to reveal the infected part of the lungs and diagnose COVID-19 with 92.4% accuracy.

The research is available to the general public to design a highly accurate and practical solution for detecting COVID-19 cases and improving treatment plans.

Thus, CV imaging data helps prevent disease spread by detecting masked faces, screening for germs, and using thermography to reveal temperature differences in a body or object.

7. Hygiene Compliance at Hospital with CV

Computer vision is a powerful tool for maintaining hospital hygiene and ensuring compliance with safety protocols. Automating the inspection of patient rooms and surfaces can detect dust, dirt, and other contaminants that pose health risks to both patients and medical staff. Utilizing artificial intelligence and deep learning, these systems assess surfaces for cleanliness, monitor disinfection activities, and pinpoint areas that require attention.

Computer vision can also track human behavior. It can detect if hospital staff forget to sanitize their hands or if visitors enter restricted areas without proper protective gear. By automating these checks, hospitals can respond quickly to hygiene lapses and enhance overall patient safety.

8. Healthcare Research & Drug Discovery

Computer vision can serve as a valuable tool for interpreting complex medical imaging and making more informed decisions.

CV algorithms can monitor patients participating in clinical trials. When integrated with electronic health records (EHRs), it enables seamless access to patient data and promotes effective collaboration across multidisciplinary healthcare teams.

This system enhances patient selection, recruitment, and retention throughout the trial process. It also helps reduce the overall cost of clinical trials and accelerates the FDA approval timeline for new drug therapies.

The technology also has its application in new medicine development. Creating a new drug is a highly time-consuming, complex, and costly process, and takes around 10-15 years. Failures are common and carry significant financial consequences. AI-driven drug discovery offers an innovative approach that may save time and financial resources in the development of new medicines.

9. Computer Vision for Enhancing Administrative Processes

Finally, by utilizing a CV in the healthcare system, numerous manual administrative processes can be easily automated. Among them are patients’ health records that should be reviewed and updated by doctors, protocol recordings, insurance documentations, and similar.

Another benefit of computer vision in healthcare is the reduction in workload. Traditional image analysis is time-consuming and labor-intensive. However, computer vision applications can optimize this process, enabling medical workers to focus on more complex cases.

Computer vision technology enhances the efficiency and accuracy of healthcare workflows, significantly reduces costs, and improves patient care and treatment outcomes.

Radiology, cardiology, dermatology, orthopedics, ophthalmology, telemedicine, and pharmaceutical research are the primary application areas. On the other hand, computer vision technology for healthcare systems may assist in multiple areas:

Provide more accurate diagnosis and health monitoring.
Develop personalized medicine.
Detect illnesses and conditions that are difficult to identify.
Create infrastructure for future research and clinical trials.
Enhance decision-making and support the prescription of appropriate treatment.
Optimize medical administrative tasks (generating automated protocols and reports).

CV Applications in Healthcare: Technical Challenges and Limitations

While the potential of computer vision in healthcare is immense, several technical challenges and limitations must be addressed.

CV and image processing can encounter failures when devices malfunction due to software bugs or viruses. Additionally, differences in an object’s size, angle, or distance from the camera can impact how it appears, causing distortions and inconsistent recognition.

Here we’ll outline the key challenges associated with applying computer vision in healthcare settings.

1. Data Privacy and HIPAA Compliance

Data privacy is one of the primary barriers to integrating deep learning algorithms. Healthcare datasets contain the most sensitive information, making it crucial to implement secure frameworks and infrastructure that comply with HIPAA or similar regulations.

Overcoming these obstacles and ensuring that patient data is secured and protected against unauthorized access opens up numerous possibilities for utilizing artificial intelligence.

2. The Need for Datasets to Train AI Models

A significant amount of healthcare data is necessary to train computer vision and deep learning systems. Healthcare computer vision requires datasets with accurate annotations of medical imaging, which can be challenging to obtain due to the diversity, privacy, and sensitivity of healthcare data.

Only when sufficient high-quality data is available is it possible to develop a robust AI-based system that recognizes patterns and abnormalities. Bias in training data is also a significant challenge for data scientists. Computer vision algorithms can hold existing biases if the training data is not diverse and representative of the population.

Thus, all healthcare entities should unite forces to collect, assemble, and unify anonymized datasets that contain information on health conditions and various demographics, thereby improving model accuracy.

3. Integration with Legacy Medical Systems

Integration with legacy systems presents another barrier to the broader adoption of healthcare computer vision. Many healthcare organizations rely on established systems that may not be compatible with new AI-driven solutions.

Overcoming these integration challenges often requires a step-by-step approach tailored to cover the needs of existing systems within a healthcare setting. Additionally, it is essential to offer post-launch employee training programs to optimize the benefits of artificial intelligence utilization.

To conclude, another significant obstacle to CV and digital health is the lack of AI and ML professionals with the necessary practical experience and knowledge to ensure the systems function properly and fully utilize computer vision systems.

Future Scope of Computer Vision in Healthcare

The primary goal of computer vision in healthcare is to develop systems that can understand, interpret data, and act in a manner similar to humans in the medical domain.

CV technology can help doctors analyze health vitals and fitness measures for more informed, precise, and quicker diagnosis. For instance, AI-powered software can convert images into interactive 3D models to aid in evaluating health conditions and diagnoses.

These are a few aspects that define the evolution of CV technology in healthcare:

The manufacturing of more powerful graphics processors and video adapters continues to increase processing power, enabling real-time image classification and recognition to occur significantly faster.
A growing number and quality of health databases, combined with the advancement of deep learning algorithms, will further facilitate the development of CV-enabled applications with higher accuracy and a greater level of detail.
More CV-enabled apps will move to the edge, meaning that the solution will operate locally, on terminal devices. This way, apps can deliver instant replies to medical image analysis, eliminating the need to wait for cloud data processing.
Convolutional neural networks and machine learning algorithms provide automated, accurate medical image analysis and reporting. This results in considerable time savings, output maximization, and the removal of human mistakes.

Also, regulatory acceptance of AI/ML technologies is growing. The FDA has approved numerous AI/ML-enabled medical devices, indicating a wider adoption of computer vision technologies in the healthcare domain.

To conclude, the future of computer vision in healthcare appears promising and is poised to play a significant role in transforming the industry. Artificial intelligence, machine learning, and computer vision are advancing patient care, diagnostics, and treatment.

Concluding Thoughts on Computer Vision Technology in Healthcare

The journey towards a more advanced and efficient healthcare system is just beginning, and computer vision is at the forefront of this exciting transformation.

The availability of large digital data volumes plays a crucial role in leveraging CV-based software in healthcare. This factor ensures high-quality medical services and an optimized administration system.

Computer vision in healthcare is revolutionizing by enhancing diagnostics, patient monitoring, surgical assistance, and pathology analysis. The technology improves patient care and treatment, laying the foundation for a more efficient and accurate healthcare system.

To summarize, the computer vision for healthcare systems may assist in:

Providing more accurate diagnoses and health monitoring.
Developing personalized treatments and medicine.
Detecting diseases and health conditions that may be difficult to find in early stages.
Creating infrastructure for future research and clinical trials.
Enhancing decision-making and facilitating the prescription of appropriate treatment.
Optimizing medical administrative tasks.

At It-Jim, we invite you to explore how computer vision can enhance your healthcare practice. We can help with a custom CV and AI solution development, meeting your requirements. By embracing these technologies, you can achieve better care quality, enhance patient safety, and deliver improved patient outcomes.

Let’s build the project together and explore opportunities to integrate the latest technologies.

Course on Computer Vision

Posted on November 24, 2020 by admin

We are happy to present our starter pack course in computer vision for ML/DL engineers and software developers who want to dive into the world of visual intelligence! Very short (2 lectures and 2 workshops), but extremely useful content created by It-Jim team. Kudos to Pavlo Vyplavin, our brain center and CTO, who accepted the challenge.

Applications of Artificial Intelligence in Automotive Industry

Posted on November 1, 2020 by admin

A century ago, the very thought of machines being able to think, make complicated calculations, and come up with effective solutions to pressing problems was more of a figment of science fiction writer’s fantasy rather than a foreseeable reality. Still, as we move into the third decade of the 21st century, we cannot imagine our life without manufacturing robots, marketing and stock trading bots, virtual travel agents, smart assistance, and other things that wouldn’t have come into existence without the achievements in artificial intelligence and machine learning. The role of artificial intelligence and machine learning in the automotive industry is also difficult to underestimate. According to recent reports, the global automotive AI market is poised to grow to $15 billion within the next five years, and for good reason. With AI driving bringing more applications to the automotive industry, more companies decide to deploy AI and machine learning models in the production environment. In today’s article, we’re going to take a closer look at the ways artificial intelligence is transforming the automotive industry and serves its current needs.

A Few Words about Artificial Intelligence

Want to learn more about artificial learning and deep learning in the automotive industry? Let’s first take a closer look at the definitions and main objectives of these branches of computer science.

Artificial Intelligence science along with its well-known machine learning and deep learning branches pursue concrete goals, which can be inferred from its very name. AI aims to enable machines to carry out the functions and complete the tasks which are normally performed by humans. In essence, AI is a machine with the ability to solve problems hitherto solved by us, humans with our natural intelligence. To evolve to strong AI machines need to learn. When machines can extract meaningful conclusions from large volumes of data sets, they start to demonstrate the ability to learn deeply. Deep learning requires artificial neural networks that operate similarly to biological neural networks in humans. The three technologies now help scientists and analysts interpret tons of data and are hence indispensable for the field of data science. And now it’s high time we discussed the significance of artificial intelligence and data science in the automotive industry.

Artificial Intelligence and Production of Vehicles

AI is having a large impact upon the automotive sector. We see AI as part of Industry 4.0 initiatives driving up efficiencies in manufacturing plants by improving overall equipment effectiveness, reducing defects, and improving automation on the line. AI is a value-add to data. This means that the manufacturer needs to have a good data environment or a route to a good data environment. Most of the data collection software installed in the last 20 years will have a good set of sensors on them. The collection of data, as well as data science applications in the automotive industry, is extremely important from a holistic point of view. Presently, lots of companies that provide AI services enable automotive businesses to improve their data environment to reach the state where they can leverage AI and realize the value from their data. The automotive sector can also benefit from good AI solutions capable of acting in advance of real time. This can help companies reduce costing, shipment, and robotic weld defects significantly. Automating visual inspection with the help of AI can in turn go a long way in reducing human error in the process and improve traceability.

Self Driving Cars

When speaking of automotive machine learning projects, it’s impossible not to mention self-driving car solutions. Major technology companies like Lift and Waymo, as well as the automakers like Toyota and General Motors, have spent billions of dollars developing self-driving cars. Autonomous buses and shuttles are currently being deployed in cities and airports, driverless trucks are already delivering goods long distances, and even autonomous flying taxis seem to be our near future. And there’s a good reason for this rapid integration of machine learning in the automotive industry.

First of all, self-driving cars will greatly reduce transportation costs for consumers. And by using autonomous fleets of shared electric cars we’d only need ten percent of cars on the road currently, which can help to significantly reduce CO2 emissions. When that shift happens, people will be able to redesign cities and create a safer environment for everyone. The data from the National Highway Traffic Safety Administration indicate that more than 90 percent of car accidents are caused by human error. This means self-driving cars have the potential to save more lives than airbags, seat belts, and stability control combined.

Although implementing ML in the automobile industry is an expensive technology, there’s definitely room for startups in this space that can create software and collect data needed to scale autonomous vehicles globally. They aim to make these cars safer by gathering data from human drivers. And there’s a big space to combine blockchain technology with fleets of these cars to create even more autonomous systems which Porsche has started trying out to increase the transparency of the decisions made by driverless cars.

Lots of people wonder how driverless cars can recognize potential threats and react to the environment in real time. Probably, you’ve heard of self-driving cars using neural networks, specific algorithms that power autonomous vehicle perception. Exactly these neural networks enable driverless vehicles to orient themselves on the street and avoid collisions.

Computer Vision

Self-driving vehicles have five core components that help them navigate and maneuver through street traffic. Computer vision is the first step in that pipeline. Whereas humans rely on eyes and brain to handle the steering wheel, whereas out driverless counterparts take advantage of computer vision. Driverless cars use computer images to find lane lines and track other vehicles on the road. The majority of autonomous vehicles utilize lots of cameras to monitor the environment in the most effective way. Tesla, for example, equips its cars with eight surround cameras that provide 360-degree visibility of the area about 490 feet around the car. There are so many tasks that cameras enable, like lane finding, road curvature estimator, obstacle detection, stop sign classification, traffic light detection, and much more.

Sensor Fusion

Now that we’ve learned so much about computer vision in the automotive industry, it’s about time we took a look at other components. As good as cameras are, there are certain measurements like distance and velocity at which other sensors excel. And some sensors can work better in adverse weather. By combining all other sensor data, we get a better understanding of the world. There are different sensors for different use cases. Thus, radar is good for determining how far away the object and how fast it’s going. Lidar, in its turn, emits an array of laser beans creating a 3D-point cloud and serves as an effective media between a camera and radar. Ultrasonic sensors, on the other hand, have a small sensing distance which makes them useful for lateral movements like parking.

Localization

Localization is how driverless cars figure out what their position in the world is. Our phones are equipped with GPS, so they help us orient ourselves in the unfamiliar terrain. For cars, more sophisticated algorithms are used, though. They help a car localize itself in a given map with the accuracy of 3, 93 inches by matching the point cloud it sees to the point cloud that the map has.

Path Planning

The car charts a trajectory through the world to get to where it wants to go. First, it needs to predict what the other vehicles around it will do to decide which maneuver to take in response to the situation. Lastly, the trajectory is built to execute the maneuver safely.

Control

Once the car has a trajectory, it has to turn the steering wheel and hit the throttle or brake accordingly to follow that trajectory. When we have an idea of the path we want our cars to follow, we try to control it. At times, controlling a vehicle can be quite tricky, like attempting a hard turn at high speed. This is something race car drivers are good at, and computers now try their best not to fall behind.

With more industries acknowledging the importance of AI, more self-driving car projects using machine learning are being created on a daily basis. It’s a rare person who would deny the fact that artificial intelligence in car systems is a perfect tool for more than making machines smarter and predicting their failures and malfunctions. Even though challenges still exist, different fields within the automotive industry are already harnessing the potential of the aforementioned techs and seeing increased efficiency and optimization of processes.