Connect with us

Artificial Intelligence

The Main Purpose Of Image Annotations Is To Develop AI Model

Published

on

The Main Purpose Of Image Annotations Is

The main purpose of image annotations is to highlight or capture the targeted object in a picture to make it recognizable for machines. Actually, image annotation is done for machine learning and AI to use the annotated images as a training data set while feeding with a deep learning algorithm and develop a fully functional AI model.

Objective Of Image Annotation

image annotation for object detection

The machine learning is the processes to develop the AI-based models that can work itself without human intervention. And to train such machines a huge amount of training data is required for computer vision to identify the objects in images from the particular industry helping machines to recognize the similar images when used in real-life.

Also Read: What Is The Use And Purpose Of Video Annotation In Deep Learning?

Types of Image Annotations

types of image annotation

There are different types of image annotation techniques used to annotate such picture. And there are different types of free tools and software are available in the market to annotate the images as per the object dimension and requirement of the project. Actually, image annotation is important for machine learning and manual image annotation service is also available in the market you can choose as per your needs.

Also Read: Why Human Annotated Datasets is Important for Machine Learning?

Bounding boxes, Semantic Segmentation, 2D or 3D Cuboid, Polygons, Polylines or Splines, Point and Landmark annotations are popular types of image annotation techniques used in this field to make the machine learning and AI model development possible.

Anolytics.ai is one of the companies, providing the best quality image annotation service with human-powered data labeling and image recognition service at best quality.

Applications of Image Annotation

The main purpose of image annotations is to make the object recognizable for machines or computer vision. And there are many industries adopting AI and integrating the related technology into their sub-fields for automation to work faster with more efficiency.

image annotation for object detection

A self-driving car is one of the best examples where annotated images have been used to train the model working into the auto functioning of such vehicles. The autonomous vehicles recognize the object on the street and move accordingly to avoid any crash.

It is like making a child learn about new things with varied data sets so that it can be recognized easily when used in autonomous vehicles to drive on the road into real-world.

medical image annotation

Healthcare is another important filed where annotated medical imaging used to develop such machines that can detect various types of maladies including life-threatening diseases like cancer at the initial stage of developments with better accuracy.

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary?

Medical images like X-ray, MRI and CT Scan reports are annotated manually by humans that are used to train the machine learning to detect such diseases without the help of humans.

Also Read: How Does Google AI Detect Breast Cancer Better Than Radiologists?

Apart from autonomous vehicle driving, there are many other fields, like healthcare, retail, agriculture, security surveillance and sports analytics are other areas image annotation is playing an important role to make the images easily recognizable for machines allowing machine learning developers to build a right model at affordable pricing.

training data for retail

The main purpose and objective of image annotation are well-described here with a various set of examples showing the use and purpose of image annotation in machine learning and AI. The right applications of image annotation are only possible when annotations are accurate so that models can get accurate data sets to learn and give the right predictions.

Advertisement
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Artificial Intelligence

Artificial Intelligence in Robotics: How AI is Used in Robotics?

Published

on

AI in Robotics

Robots were the first-known automated type machines people got to know. There was a time when robots were developed for performing specific tasks, yes such machines were earlier developed without any artificial intelligence (AI) to perform only repetitive tasks.

But now the scenarios are different, AI in getting integrated into robots to develop the advanced level of robotics that can perform multiple tasks, and also learn new things with a better perception of the environment. AI in robotics helps robots perform the crucial tasks with a human-like vision to detect or recognize the various objects.            

Nowadays, robots are developed through machine learning training. A huge amount of datasets is used to train the computer vision model, so that robotics can recognize the various objects and carry out the actions accordingly with right results.       

And, further, day-by-day, with more quality and precise machine learning processes, robotics performance is getting improved. So, right here we are discussing the machine learning in robotics and types of datasets used to train the AI model developed for robots.

How AI is Used in Robotics?

The AI in robotics not only helps to learn the model to perform certain tasks but also makes machines more intelligent to act in different scenarios. There are various functions integrated into robots like computer vision, motion control, grasping the objects, and training data to understand physical and logistical data patterns and act accordingly.    

And to understand the scenarios or recognize the various objects, labeled training data is used to train the AI model through machine learning algorithms. Here, image annotation plays a key role in creating a huge amount of datasets helping the robotics to recognize and grasp different types of objects or perform the desired action in the right manner making AI successful in the robotics.     

Application of Sensors in Robotics

The sensor helps the robots to sense the surroundings or perceive the visuals of the environment. Just like five key sensors of human beings, combinations of various sensing technologies are used in the robotics. From motion sensors to computer vision for object detection, there are multiple sensors providing a sensing technology into changing and uncontrolled environments making the AI possible in the robotics. 

Uses of Types of Sensors in Robotics:

  • Time-of-flight (ToF) Optical Sensors
  • Temperature and Humidity Sensors
  • Ultrasonic Sensors
  • Vibration Sensors
  • Millimeter-wave Sensors

Nowadays a wide range of increasingly more sophisticated and accurate similar sensors, combined with systems that can fuse all of this sensor data together is empowering robots to have increasingly good perception and awareness for the right actions in real-life.  

Application of Machine Learning in Robotics

Basically, machine learning is the process of training an AI model to make it intelligent enough to perform specific tasks or some varied actions. And to feed the ML algorithms, a set of data is used at a large scale to make sure AI models like robotics can perform precisely. As much as training data will be used to train the model, the accuracy would be at the best level. 

In robotics, it is trained to recognize the objects, with the capability to grasp or hold the same object and ability to move from one location to another location. Machine learning mainly helps to recognize the wide-ranging objects visible in different shapes, sizes and various scenarios.

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And the machine learning process keeping running if robots detect new objects, it can make the new category to detect such objects if visible again in the near future. However, there are different disciplines of teaching a robot through machine learning. And deep learning is also used to train such models with high-quality training data for a more precise machine learning process.  

APPLICATION OF AI IN ROBOTICS

AI in robotics makes such machines more efficient with self-learning ability to recognize the new objects. However, currently, robotics are used at the industrial purpose and in various other fields to perform the various actions with the desired accuracy at higher efficiency, and better than humans.

Video: Most Advance AI Robots

From handling the carton boxes at warehouses, robotics is performing the unbelievable actions making certain tasks easier. Right here we will discuss the application of AI robotics in various fields with types of training data used to train such AI models.    

Robotics in Healthcare

Robotics in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.

AI Robotics in Healthcare

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

From medical supplies, to sanitization, disinfection and performing the remote surgeries, AI in robotics making such machines become more intelligent learned from the data and performs various crucial tasks without the help of humans.

Robotics in Agriculture

AI Robotics in Agriculture

In the agriculture sector, automation is helping farmers to improve crop yield and boost productivity. And robotics is playing a big role in the cultivation and harvesting the crops with precise detection of plants, vegetables, fruits, and other unwanted floras. In agriculture AI robots can perform the fruits or vegetable plucking, spraying the pesticides, and monitor the health conditions of plants.

Also Read: HowAI Can Help In Agriculture: Five Applications and Use Cases

Robotics in Automotive

AI in Robotics in Automotive

The automobile industry moved to the automation that leads to fully-automated assembly lines to assemble the vehicles. Except for a few important tasks, there are many processes performed by robotics to develop cars reducing the cost of manufacturing. Usually, robotics is specially trained to perform certain actions with better accuracy and efficiency.

Robotics at Warehouses

AI Robotics at Warehouses

Warehouse needs manpower to manage the huge amount of inventory kept by mainly eCommerce companies to deliver the products to their customers or move from location to another location. Robotics is trained to handle such inventories with the capability to carefully carry from one place to another place reducing the human workforce in performing such repetitive tasks.

Robotics at Supply Chain

AI Robotics at Supply Chain

Just like inventory handling at warehouses, Robotics at logistics and supply chain plays a crucial role in moving the items transported by the logistic companies. AI model for robotics gets trained through computer vision technology to detect various objects. Such robotics can pick the boxes and kept at the desired place or load and unload the same from the vehicle at faster speed with accuracy.

Training Data for Robotics    

As you already know a huge amount of training data is required to develop such robots. And such data contains the images of annotated objects that help machine learning algorithms learn and recognize the similar objects when visible in the real-life.

Also Read: Top 5 Applications of Image Annotation in Machine Learning & AI

And to generate a huge amount of such training data, image annotation techniques are used to annotate the different objects to make them recognizable to machines. And Anolytics provides the one-stop data annotation solution to AI companies to render high-quality training data sets for machine learning-based model development.      

Also Read: What Is The Use And Purpose Of Video Annotation In Deep Learning

Continue Reading

Artificial Intelligence

Artificial Intelligence in High-Quality Embryo Selection for IVF

Published

on

artificial intelligence embryo selection IVF

IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.

As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.

As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.

Risk Factors in IVF Treatment

While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.

artificial intelligence in ivf

Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.

Also Read: How Artificial Intelligence Can Predict Health Risk of Pregnancy

Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.

artificial intelligence embryo selection

There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.

AI in Embryo Selection through Machine Learning

The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.

ivf machine learning
AIVF’s deep learning and computer vision algorithms applied to time-lapse videos and stills of embryo development with proprietary markers and identifiers. Image Credit

The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.

Also Read:  What Causes A Baby To Stop Growing In The Womb During Pregnancy

The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.

However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions. 

Machine Learning Training Data for AI Model

The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.

Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.

Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.

Watch Video:  Future of AI in Embryo Selection for IVF

Source: Health Analytics

Continue Reading

Artificial Intelligence

How Artificial Intelligence Can Predict Health Risk of Pregnancy?

Published

on

artificial intelligence pregnancy risk

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).

Placenta Complications During Pregnancy

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.

Also Read: What Causes A Baby To Stop Growing In The Womb During Pregnancy

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.

machine learning predict pregnancy
Image and blood vessel patches from data set: Image Source

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.

artificial intelligence pregnancy risk

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.

Also Read: Artificial Intelligence in High-Quality Embryo Selection for IVF

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.     

Also Read: Why Global Fertility Rates are Dropping; Population Will Fall by 2100

Source: Health Analytics

Continue Reading
Advertisement

Latest Posts

Advertisement
Advertisement
Advertisement
Advertisement
Advertisement
Advertisement

Trending

en English
X