Thanks to AI and machine learning, computer vision technology is getting upgraded with improved versions of visualizing making perception through machines reliable. Actually, this is completely related to computer-based visual processing of objects.
What is Computer Vision in Machine Learning and AI?
Computer vision is simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model to recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.
The main purpose of using computer vision technology in ML and AI is to create a model that can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.
How Does Computer Vision Work?
Computer vision in machine learning is used for deep learning to analyze the data sets through annotated images showing an object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.
This process depends subject to the use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the predictions accurately in the future. Computer vision can be only utilized only with image processing through machine learning.
How Computer Vision is Different from Image Processing?
Both are part of the AI technology used while processing the data and creating a model. The difference between computer vision and image processing in computer vision helps to gain high-level understanding from images or videos.
For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.
Whereas, image processing doesn’t need such a high level of understanding of image. In fact, it is the sub-field of signal processing but also applied to images. For example, if you have noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.
The image process task involves filtering, noise removal, edge detection, and color processing. In entire processing, you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.
The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.
Application and Role of Computer Vision in AI and ML
The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.
From simple home tasks to recognizing human faces, detecting the objects in autonomous vehicles, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.
The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flying like drones and manufacturing, etc.
Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.
Many companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning.
Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model to perform accurately in real-life use.
How Artificial Intelligence Can Predict Health Risk of Pregnancy?
Artificial Intelligence (AI) in healthcare is going to improve the birth process of humans with better diagnosis method when baby is in mother’s womb. Yes, using the machine learning approach, now AI can help predict the pregnancy related risks.
As per the published in the American Journal of Pathology, a machine learning model can analyze placenta slides and inform more women of their health risks in future pregnancies, leading to lower healthcare costs and better outcomes.
Placenta Complications During Pregnancy
Actually, when a baby is born, doctors sometimes examine the placenta for features that might suggest health risks in any future pregnancies. Providers analyze placentas to look for a type of blood vessel lesion called decidual vasculopathy (DV).
These indicate that the mother is at risk for preeclampsia, a complication that can be fatal to both the mother and baby in any future pregnancies. Once detected, preeclampsia can be treated, so there is considerable benefit from identifying at-risk mothers before symptoms appear.
However, although there are hundreds of blood vessels in a single slide, only one diseased vessel is needed to indicate risk. This makes examining the placenta a time-consuming process that must be performed by a specialist, so most placentas go unexamined after birth.
How Machine Learning Predict Pregnancy Risks?
Researchers said, pathologists train for years to be able to find disease in these images, but there are so many pregnancies going through the hospital system that they don’t have time to inspect every placenta with full attention and accuracy.
While on the other hand researchers trained a machine learning algorithm to recognize certain features in images of a thin slice of a placenta sample. The team showed the tool various images and indicated whether the placenta was diseased or healthy.
Because it’s difficult for a computer to look at a large picture and classify it, the team employed a novel approach through which the computer follows a series of steps to make the task more manageable.
First, the computer detects all blood vessels in an image. Each blood vessel can then be considered individually, creating similar data packets for analysis.
Then, the computer can access each blood vessel and determine if it should be deemed diseased or healthy. At this phase, the algorithm also considers features of the pregnancy, such as gestational age, birth weight, and any conditions the mother might have. If there are any diseased blood vessels, then the picture is marked as diseased.
The tool achieved individual blood vessel classification rates of 94% sensitivity and 96% specificity, and an area under the curve of 0.99. While algorithm helps pathologists know which images they should focus on by scanning an image, locating blood vessels, and finding patterns of the blood vessels that identify.
The team noted that the algorithm is meant to act as a companion tool for physicians, helping them quickly and accurately assess placenta slides for enhanced patient care.
AI Assisted Pregnancy Risk Detection
Though, this algorithm isn’t going to replace a pathologist anytime soon. The goal here is that this type of algorithm might be able to help speed up the process by flagging regions of the image where the pathologist should take a closer look.
Such studies demonstrate the importance of partnerships within the healthcare sector between engineering and medicine as each brings expertise to the table that, when combined, creates novel findings that can help so many individuals.
Such useful findings have significant implications for the use of artificial intelligence in healthcare. As healthcare increasingly embraces the role of AI, it is important that doctors partner early on with computer scientists and engineers so that we can design and develop the right tools for the job to positively impact patient outcomes.
And with the high-quality healthcare training data for machine learning can further help to improve the risks level associated with pregnancies. AI companies are using the right training datasets to train such model to learn precisely and predict accurately.
Source: Health Analytics
What is Medical Image Annotation: Role in AI Medical Diagnostics
AI in healthcare is becoming more prevalent with more effective computer vision-based machine learning model developments. The more training data is used with the machine learning algorithm the AI model will learn with more variations making it easier to predict the results with more accuracy in various scenarios for the healthcare sector.
And to make the training data useful and productive, the annotated medical images are used to make the disease or body aliments detectable through machines. Medical image annotation is the process used to create such data with an acceptable level of accuracy.
What is Medical Image Annotation?
Medical image annotation is the process of labeling the medical imaging data like Ultrasound, MRI, and CT Scan, etc. for machine learning training. Apart from these radiologist images, other medical records available in the text formats are also annotated to make it understandable to machines through deep learning algorithms for accurate predictions.
Medical image annotation is playing an important role in the healthcare sector, so right here we will discuss the importance and role of the medical image annotation. And what is the types of medical images can be annotated to create the training data sets for the different disease.
Role of Medical Image Annotation in AI Medical Diagnostics
Medical image annotation is playing a big role in detecting the various types of diseases through AI-enabled devices, machines and computer systems. Actually, this process provides the real information (data) to the learning algorithms, so that model becomes user to detect such diseases when similar medical images put in front of the system.
From normal bone fracture to deadly disease like cancer, medical image annotation can detect the maladies at microscopic level with accurate predictions. Hence, you can find here the types of diseases or diagnosis performed by AI in medical imaging diagnostics, trained through set of data generated through medical image annotation.
Diagnosis the Brain Disorders
Medical image annotation is used to diagnosis the disease including brain tumors, blood clotting, or other neurological disorders. Using the CT Scan or MRI, machine learning models can detect such diseases if well-trained with precisely annotated images.
AI in neuroimaging can be possible when brain injuries and other ailments are properly annotated and feed into the machine learning algorithm for the right prediction. Once the model, get fully trained to it can be used on the place of radiologist making with the better and more efficient medical imaging diagnosis process saving the time and efforts of the radiologist in taking other decision.
Diagnosis the Liver Problems
Liver related problems and complications diagnosed by the medical professionals using the ultrasound images or other medical imaging formats. Usually, physicians detect, characterize, and monitor diseases by assessing liver medical images visually. And in some cases, he can be biased due to his personal experiences and inaccuracy.
While medical image annotation can train the AI model to perform the quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.
Detecting the Kidney Stones
Similarly, Kidney related problems like infection, stone, and other ailment affecting the functioning of the kidney. Though AI applications in kidney disease is currently no significant but right now it is mainly focused on various key aspects like Alerting systems, Diagnostic assistance, Guiding treatment, and Evaluating prognosis.
And when the algorithms get the right annotated data sets of such images, the model comes capable enough to even diagnosis the possibilities if kidney failure. Apart from bounding box annotation, there are various other popular medical image annotation techniques used to annotate the images making AI possible in detecting the kidney related to various problems.
Detection of Cancer Cells
Detecting cancers through AI-enabled machines is playing a big role in saving people from such life-threatening diseases. When cancer is not detected at the initial stage, it becomes incurable or takes extraordinary time to cure or recover from such illnesses.
Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively, globally with high death rates among both genders. But now AI models trained with medical image annotation can help machine learning models to learn from such data and predict with the condition of maladies due to cancer.
Teeth Segmentation for Dental Analysis
Teeth or gums related problems can be better diagnosed with AI-enabled devices. Apart from teeth structure, AI in dentistry can easily detect various types of oral problems. Yes, a high-quality training data set, can help the ML algorithm recognize the patterns and store in its virtual memory to use the same patterns in the real-life.
Medical image annotation can provide high-quality training data to make the AI in Dentistry possible with quantitative and qualitative data used to train the model and accuracy will improve in machine learning for dental image analysis.
Eye Cell Analysis
Eyes scanned through retinal images can be used to detect various problems like ocular diseases, cataracts, and other complications. All such symptoms visible in the eyes can be annotated with the right techniques to diagnosis the possible disease.
Microscopic Cell Analysis
It is impossible to see the microscopic cells with normal human eyes, buy using the microscope it can be easily seen. And make such extremely small size cells recognizable to machines, the high-quality image annotation technique is required for right model development.
The images of these microscopic cells are enlarged on the bigger computer screen and annotated with advanced tools and techniques. And while annotating the images, the accuracy is ensured at the highest level to make sure the AI in healthcare can give precise results. Our experts can label microscopic images of cells used in the detection and analysis of diseases.
Diagnostic Imaging Analysis
Diagnostic imaging like X-ray, CT & MRI scan gives the better option to visualize the disease to find out the actual condition and provide the right treatment. Our experts in the image annotation team can generate imaging and label specific disease symptoms using diverse annotation techniques.
Medical image annotation is giving the AI in radiology a new dimension with a huge amount of label data for the right machine learning development. And for supervised machine learning, annotated images are must to train the ML algorithms for the right diagnostic imaging analysis.
Medical Record Documentation
Medical image annotation also covers the various documents including texts and other files to make the data recognizable and comprehensible to the machine. Medical records contain the data of patients and their health conditions that can be used to train the machine learning models.
Annotating the medical records with text annotation and precise metadata or additional notes makes such crucial data used for machine learning development. Highly experienced annotators can label such documents with a high level of accuracy while ensuring the privacy and confidentiality of data.
Types of Documents Annotated through Medical Image Annotation:
- CT Scan
- Other Images
To annotate such highly sensitive documents with acceptable levels of accuracy, and AI medical diagnostics companies need a huge amount of such data to train the AI model for the right prediction. Cogito offers the world-class medical image annotation service to annotate the medical image dataset for AI in healthcare. It can annotate the huge amount of radiology images with high-level accuracy.
Cogito offers a great platform to generate a huge amount of training data sets for AI in various industries and sectors. AI companies seeking high-quality training data for machine learning development into wide-ranging fields like healthcare, retail, automotive, agriculture, and autonomous machines can get the best quality training datasets available here at the best pricing.
This article was originally written & published for Cogito Tech
5 Levels of Autonomous Vehicles & Challenges of Self-Driving Cars
Autonomous vehicles, especially self-driving passenger cars are like a dream when it will become come true. Yes, I’m talking about the time full-fledged deployment of artificial intelligence into a car that can run automatically on the busy road in various scenarios, without driver’s assistance avoiding all the objects making the journey safe and crash-free.
Yes, till now except few commercial vehicles, there are no self-driving cars runs at the fully automotive mode. Though a few years back Google and Tesla have successfully tested autonomous vehicles, even Tesla motors provide a different level of autonomy but were not successful enough, due to few accidents that happened while on testing and at the time of real-life use by the car owners.
Do you know why autonomous vehicles are still not on the road, or what are the reasons it is taking this much time to make such vehicles successfully run on the road? Many small problems are working with such technology. And then there’s the challenge of solving all those small problems and putting the whole system together to work ADAS technology.
There are different levels of autonomy in the self-driving cars allowing the driver to control the key functions or depend on the machine to make its own decision. So, right here before we discuss the challenges of autonomous vehicles we need to know about the different levels of autonomy – a self-driving car use to run on the road.
5 Levels of Autonomous Driving
Level 0: This level, you can say nothing to do with automation, means, all the systems like steering, brake, throttle, and power are controlled by humans.
Level 1: Yes, the level of automation starts from this stage. At this stage of autonomy, most of the functions are still controlled by the driver, but a specific function (like steering or accelerating) can be done automatically by the car.
Level 2: In this stage of automation, at least one driver-assistance system is automated like acceleration and steering, but requires humans for safe operation. Actually, at this level, the driver is disengaged from physically operating the vehicle.
Level 3: At the third level of automation, many functions are automated. Yes at this stage the car can manage all safety-critical functions under certain conditions, but the driver is expected to take over when alerted due to uncertain conditions.
Level 4: This is the stage you can say a car is fully autonomous that can perform all the safety-critical functions in certain areas and under the defined weather conditions. But not all the functions.
Level 5: If a self-driving car is equipped with the 5th level of automation, it is a fully autonomous vehicle, capable of self-driving in every driving scenario just like humans control all the functions.
These are the most common five levels of automation, a self-driving car can be developed. If you want to enjoy a ride on a fully autonomous car, it should have the 4th or 5th level of automation. But there are many challenges in developing and running a fully autonomous car, and below we will discuss these challenges and their implications.
5 Major Problems with Self Driving Cars
Few automotive manufacturers like Tesla are already integrated certain level of automation into the cars, but not level 5 or full automation, as there are certain challenges of autonomous vehicles making difficult for the manufacturers to develop an AI-enabled fully automated car that can run without human intervention with complete safety.
Understanding the issues with self-driving cars is very important for machine learning engineers to develop such an AI-enabled vehicle for successful driving. So, right here we also discuss the most critical problems with self-driving cars.
Training AI Model with Machine Learning
As we know, to develop an autonomous vehicle, a machine learning-based technology used for integrating AI into the model. The data gathered through sensors can be understood by cars only through machine learning algorithms.
These algorithms will help identify objects like a pedestrian, a street light detected by the sensors and classify them, as per the system’s training. And then, the car uses this information to help decide whether the car needs to take the right action to move, stop, accelerate, or turn aside to avoid a collision from objects detected by the sensors.
And with the more precise machine learning training process, in near future machines will be able to do this detection and classification more efficiently than a human driver can. But right now there is no widely accepted and agreed basis for ensuring the machine learning algorithms used in the cars. There are no such agreements across the automotive industry how far machine learning is reliable in developing such automated cars.
Open Road with Unlimited Objects
Autonomous cars run on the road, and once it starts driving, machine learning helps it learn while driving. And while moving on the road, it can detect various objects that have not come across while training and be subject to software updates.
As the road is open, and there could be unlimited or multiple types of new objects visible to cars, that have been not used to train the self-driving car model. And how to ensure that system continues to be just as safe its previous version. Hence, we need to be able to show that any new learning is safe and that the system doesn’t forget previously safe behaviors or something like this, the industry yet to reach agreement on.
Lack of Regulations and Standards
Another hurdle for the self-driving car is there are no specific regulations or sufficient standards for the whole autonomous system. Actually, as per the current standards for the safety, for existing vehicles, the human driver has to take over the control in an emergency.
For autonomous vehicles, there are few regulations for functions like automated lane-keeping system. And there are also international standards for autonomous vehicles that include self-driving cars, which sets related requirements but not useful in solving the various other problems like machine learning, operational learning, and sensors.
Social Acceptability Among the People
Over the past year while testing or in real-life use, self-driving cars involved in the crash on autopilot mode. And such incidents discourage people to fully rely on autonomous cars due to safety reasons. Hence, social acceptability is not acceptable to such car owners but also among other people who are sharing the road while running on the road with them.
So, people need to accept and adopt the self-driving vehicle’s systems with involvement in the introduction of such new-age technology. And unless the acceptability reached social levels, more people will not use to buy self-driving cars, making it difficult for the auto manufacturers to further improve the functions and performance of such cars.
Use & Availability of Data for Sensors
To sense the surroundings of an environment, a self-driving car use a broad set of sensors like Camera, Radar, and LIDAR. These sensors help to detect varied objects like pedestrians, other vehicles, and road signs. The camera helps to view the object while on the other hand, Radar helps to detect objects and track their speed and direction.
Similarly, there is another important sensor called LIDAR that uses lasers to measure the distance between objects and the vehicle. And a fully autonomous car needs such a set of sensors that accurately detect objects, distance, speed, and so on under all conditions and environments, without a human needing to intervene.
Why LIDAR for Autonomous Vehicle?
All these sensors feed all gathered data back to the car’s control system or computer to help it make decisions about where to steer or when to brake and turn in the right direction. Uncertain environment conditions like Lousy weather, heavy traffic, road signs with graffiti on them can all negatively impact the accuracy of sensing capability.
Video: Why LiDAR is used for Autonomous Vehicles?
Here, Radar sense is more suitable, as it is less susceptible to adverse weather conditions, but challenges remain in ensuring that the chosen sensors used in a fully autonomous car can detect all objects with the required level of certainty for them to be safe. So, the LIDAR sensor is more important and precise to detect objects with range depth.
3D Point Cloud Labeling for LIDARs Sensors
To utilize the power of sensing the objects from the distance, LIDAR is no doubt the best suitable sensor for self-driving cars, but making the different types of objects and various scenarios perceivable such images are labeled through 3D Point Cloud Annotation service.
LIDAR point cloud segmentation is the technique used to classify the objects having the additional attribute that a perception model can detect for learning. For self-driving cars, 3D point cloud annotation services help to distinguish different types of lanes in a 3D point cloud map to annotate the roads for safe driving with more precise visibility in 3D orientation.
This article is originally written and submitted in Anolytics.ai
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