Why radiologists in the healthcare sector will gain from AI

In radiology field, talk of artificial intelligence (AI) has become commonplace. Many think AI, also known as machine learning or deep learning, may improve radiologists’ operations, enable quantitative radiology, and help identify genetic markers.

Radiologists are very active medical specialists. Any errors would be unaffordable. They must communicate with a broad spectrum of referring medical professionals, including neurologists, urologists, orthopedic specialists, and more. They must always be on their toes. What can AI offer these overworked radiologists to improve their performance?

What advantages does AI have for radiology?

There are various ways AI might improve radiologists’ skills even further. We will go through a number of these methods in this section. This is not a complete list. There are several other ways that AI might help radiologists. Later on, we’ll add to this area.

Specify a more specific diagnosis

The goal of many AI systems is to provide more information. This might be accomplished by quantitating image-related details or through radiology image annotation, which are now only reported qualitatively. Alternately, the software can incorporate normative values, enabling doctors to contrast patient outcomes with a mean based on a general population sample.

The problem with this benefit is that we are frequently unsure how to manage this additional information. What does a particular value indicate? What does it signify for the diagnosis when a patient deviates significantly from the general population? There are frequently no rules (yet!) on what this information implies or what a radiologist should do because we have limited experience with quantified information.

Take on rote, everyday tasks.

AI is not proficient in all areas. Not yet, at least. What are the best tasks that AI can now handle? Lessons are easy to do, for which we have a tonne of data, and that doesn’t call for mixing many various types of input. Hence, radiologists perform a lot of straightforward everyday jobs. This usually refers to the most tedious activities or tasks that radiologists find difficult.

Reduce both intra- and inter-observer variation

Even radiologists with the highest levels of training and experience can have different diagnoses. After a long day at work, anything new could capture the eye of someone refreshed in the morning. Additionally, the emphasis on the findings of various radiologists may vary.

This is a challenge for referring doctors since they must consider these variances when putting all their knowledge together to make a final diagnosis. AI algorithms can reduce or even eliminate this variation in radiologist reports.

How might AI aid radiologists in treating patients?

The best patient results are the ultimate goal of any diagnostic procedure. Since medical imaging is a growing component of the diagnostic process, it should have the same result as other diagnostic procedures: the patient’s benefit.

Therefore, we should do the litmus test and determine whether any AI tool used by radiologists to evaluate pictures benefits the patient in the long run. Expressed, the quality and efficiency axes may be used to conceptualize patient benefits. Both are covered below.

Quality improvement for improved patient results

AI has enormous potential to improve the accuracy of the picture readings we now use. For instance, by carrying out analyses that are not being carried out because they would take too long for radiologists to carry out manually. An illustration would be volumetric measurements of organs, where hand delineation would be too time-consuming yet might increase the accuracy of the diagnosis.

AI also plays a significant role in the advancement of precision medicine. We can more precisely evaluate what information suggests that particular therapies will improve patient outcomes when more patient data becomes available. Patient communication is another process stage that might benefit from some AI input.

Increasing effectiveness for the patient’s benefit

Although the level of treatment quality is crucial, excellent care is useless if the diagnosis takes too long. As a result, efficiency and quality should always be blended. Several ways AI may assist enhance productivity. The automation of time-consuming manual processes can aid in accelerating the diagnosing process.

Choose the ideal tool for medical image annotation.

Without question, machine learning has the potential to revolutionize the healthcare sector.

Wide-ranging potential applications include the whole medical imaging life cycle, from picture production and analysis through diagnosis and prognosis.

These labels may occasionally be used in machine learning (ML). However, their format is frequently in sync with ML research requirements, such as lacking instance IDs, characteristics, a labeling queue, or the proper forms for deep learning frameworks like Pytorch or TensorFlow.

Collaborating with a reputed medical annotation company that has previously expended the time and energy necessary to adhere to the numerous data formats, regulatory regulations, and user experience required for a successful medical AI project is generally advisable.

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medical annotation

Five AI Applications That are Changing our World

Artificial intelligence is concerned with finding ways for computers to perform the same functions as humans. However, computers cannot make decisions on their own, i.e., they cannot make decisions without human intervention. The ability of computers to make decisions with their own intelligence makes machines operate and think like machines, which is what, in general terminology, is said to be as artificial intelligence (AI). There are myriad applications of AI in almost every field, which appear to enhance efficiency with minimal human intervention.

How AI can Reshape Our Future

Nowadays, AI is part of our everyday lives. Alexa, Google Home, and other smart speakers provide almost accurate results when we use them. The recommendation to complete sentences can be seen in email writing. Artificial intelligence is not only used in our day-to-day lives but also in military actions, warfighting, and weaponry. With the advancement of AI in cybersecurity, security systems are now capable of identifying cyber threats and creating a counter-attack security tool to deal with them strategically.

In recent trials, DeepMind from Google has beaten doctors in detecting breast cancer. AI is playing a substantial role in healthcare and pharmaceuticals. In pharmaceuticals, there is an increasing ability to combine different outcomes in a shorter amount of time and with greater accuracy. It is clear that AI is becoming increasingly prevalent in every aspect of our lives. With this tremendous advancement in artificial intelligence, it’s quite fascinating to imagine what our future will look like.

Our future will likely be dominated by applications of artificial intelligence that will affect many facets of our economy and society; many credible experts view this era as the most promising technological innovation and value creation in the foreseeable future. With improved data annotation services and the pace of innovations around AI & machine learning, AI is said to be holding the potential for reshaping our future in many ways.

The following are the major fields in which AI can be used, and we will discuss its future in each.

AI for Better Health Care

The use of artificial intelligence is capable of detecting disease based on symptoms without the need to visit a doctor. By analyzing the fitness band data and medical history, AI would suggest suitable medications and even deliver them to the customer’s fingertips through their phones.

Detecting fatal diseases like breast cancer has already been made easier by Google’s DeepMind. Detecting common diseases and suggesting proper medications are not far off when AI detects them. Thus, setting up medical appointments for prescriptions may not require human intervention.

AI in Education

AI-backed computer vision and machine learning systems are pushing growth across all industries, including education. Even though AI-based solutions have been available in the EdTech industry for quite some time, the industry has been slow to capitalize on them. Educators, however, were forced to rely on technology for virtual learning after the pandemic drastically changed the landscape.

All students have access to learning anytime and anywhere, thanks to AI-driven tools. AI can instantly answer students’ most frequently asked questions through support automation and conversational intelligence. As an added perk, students from around the globe can attend high-quality courses without incurring travel or living expenses

AI in Finance

Artificial intelligence allows businesses to create more space and improve security while ensuring they have a technologically advanced and innovative product. Financial services can be turbo-charged using artificial intelligence, and customers’ services can be transformed. As a result, products and services could be tailored to specific needs, internal processes could be more efficient, cybersecurity could be enhanced, and risk could be reduced.

In corporate finance, artificial intelligence is beneficial as it allows for better predicting and assessing loan risks. By improving loan underwriting and reducing financial risk, AI technologies, i.e., AI-powered computer vision and machine learning models, can make business processes more efficient with automation integration. In addition to bringing better data handling and improving customer experiences, AI has the potential to simplify, accelerate, and reimagine traditional processes.

AI in Military and Cybersecurity

Armed with artificial intelligence, military technologies are developing autonomous weapon systems that do not require human intervention, thereby enhancing security. Robots that are as intelligent as soldiers/commandos and capable of performing certain tasks may become available in the near future.

It is important to note that AI-assisted strategies will enhance mission effectiveness and provide the safest and most efficient way to execute missions. However, one of the concerns with AI-assisted systems is how the algorithms are performed.

AI Applications and the Scientific Method

Scientists have been concerned about the slowdown of scientific progress in recent decades. Discoveries may not be as abundant as they once were. AI now offers the possibility of innovating existing scientific discoveries and upgrading their mechanisms for greater efficiency and ease of use. As AI and machine learning (ML) develop, the capabilities of what we can accomplish will vastly increase.

Various ideas in human thought can be explored computationally. In addition to the ideas humans can handle with computers, there are many more that computers can do. The possibilities of human success with computers and artificial intelligence are much more comprehensive. The use of artificial intelligence enables unprecedented levels of analysis and pattern discovery of vast data sets. A new golden age of scientific revolution is coming as AI augments human intelligence, revolutionizing scientific research.

Conclusion

As we discussed in the article, artificial intelligence-assisted systems have the potential to improve the quality of our lives, we can conclude what our future path of technology is going to be laid on. Companies like Cogito, Anolytics, and others are emerging to empower AI and machine learning innovations through moderation, data annotation services, and quality training data delivery for machine learning models. Seeing how impactful it will be in the future, AI holds the potential to shape how the earthly life system on earth will function in the time ahead.

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Image Annotation : overview 2022 used various type of annotation

One of the most crucial phases in creating computer vision and image recognition systems, which entails identifying, getting, characterizing, and evaluating outcomes from digital images or videos, is image annotation.

AI applications frequently employ computer vision, including security, medical imaging, autonomous cars, and others. As a result, image annotation is essential for developing AI/ML in many fields and sectors.

Before we dig deep into the topic, let us discuss some basic nuances of image annotation.

Image annotation

Adding labels to an image is known as an image annotation. These labels are set by the AI engineer and are selected to provide details about what is seen in the image to the computer vision model.

react to section with heart react to section with light react to section with money react to section with thumbs-down The number of labels on an image might be different according to the project. A single label will sufficiently convey the complete image’s information (image classification) for specific projects. Other applications need many items labeled with distinct labels inside a single photograph.

The Process of Image Annotation. How does it work?

Three crucial elements are required to annotate an image:

1- Images

2 – Adding annotations to the photos

3 – A tool for adding annotations to the photos

Finding and training annotators to complete the annotation tasks is the first step in most image annotation initiatives. Although AI is highly specialized, annotating AI training data is not always necessary. While a master’s degree in machine learning is required to build a self-driving automobile, it is not needed to draw boxes around cars in photos (bounding box annotation). As a result, few annotators hold degrees in machine learning.

However, because each organization will have distinct needs, these annotators should get in-depth training on the requirements and norms of each annotation project.

After receiving training on annotating the data, the annotation team will begin working on hundreds or thousands of photos on a platform specifically designed for image annotation. This platform’s software needs to be equipped with all the tools required for the particular kind of annotation to be done.

What methods and techniques exist for image annotation?

There are many different image annotation methods and techniques, but it doesn’t imply you should use them all. Understanding the most popular image annotation methods and techniques along with use cases will help you decide which annotation tool to use for your project and what it requires.

Bounding boxes

Rectangles are drawn around symmetrical items like furniture, vehicles, and packages using bounding boxes. This facilitates object detection and algorithm localization, which is essential for driverless cars.

Self-driving automobiles can navigate the roadways securely with annotations of people, traffic signals, and vehicles. Bounding boxes come in two and three dimensions (cuboids).

Segmentation

Segmentation goes beyond object identification and picture categorization. With this technique, a picture is divided into several parts, each of which is given a label. Since each segment consists of pixels, each pixel has a label, which increases the accuracy of the annotation.

Three categories of segmentation exist:

1 Semantic segmentation

Semantic segmentation involves grouping an image into clusters and giving each group a label. Consider that we have a picture of three sheep. Each sheep will be counted as a separate cluster for semantic segmentation. The sky will serve as the background, which will also be considered.

2 Instance segmentation

Objects’ existence, position, form, and number are determined through instance segmentation. For example, instance segmentation may count the number of persons in a picture. Let’s revisit the sheep illustration. Even though they are given the same name, each sheep will be counted as a different instance in the instance segmentation scenario.

3 Panoptic segmentation

Instance segmentation and semantic segmentation converge in panoptic segmentation. It semantically segments the image and categorizes each pixel, identifying the instances to which each pixel belongs (instance segmentation). Each sheep will be tallied independently in our case, even if all the pixels in the image will be given names.

4 Skeletal Annotation

Skeletal annotation draws attention to body alignment and mobility. Annotators use this technique to connect lines on the human body by adding dots at the locations where the lines articulate. For instance, a line from a point on the wrist to a point on the elbow is then connected to the shoulder, and so on.

This produces a condensed representation of a body’s location that computers can recognize. This approach is used for video in most sports use cases, requiring annotators to precisely identify body postures over tens of thousands of individual frames.

5 Bitmask Annotation

This annotation style permits gaps or disjointed annotations by connecting individual pixels to particular objects.

6 Polygons

The edges of objects with asymmetrical shapes, such as rooftops, plants, and landmarks, are annotated using polygons.

7 Key-points

By placing dots around the target item, such as facial characteristics, body parts, and stances, key points are used to annotate microscopic shapes and details.

To achieve certain annotation objectives, any of the aforementioned annotation approaches can be combined. AI firms have freedom and alternatives when organizing annotation projects as the company can also do customised annotations as per the requirements.

Essential applications and benefits of Image annotation

When employing computer vision (CV) models, image annotation is extremely important for machine learning and artificial intelligence.

Across sectors, the advantages and significance of picture annotation services are becoming recognized. In 2023, the market for AI and machine learning data solutions is predicted to reach $1.2 billion.

Here are some examples of how businesses in various industries use image annotation services to their advantage.

Transportation: CV is used by self-driving automobiles, which are a reality today. Advanced machine learning algorithms must power these vehicles to ensure their efficiency and safety. Automobile makers may create intelligent apps for these autonomous vehicles with picture annotation.

Healthcare: In healthcare, CV can assist increase the precision of the diagnosis and raise the standard of care. With image annotation, CV systems may use CT scans, MRIs, and other diagnostic technologies to focus on patterns and problems like tumors or hairline fractures.

Agriculture: Precision agriculture combines established farming practices with cutting-edge technology to increase profitability, production, and sustainability. CV systems make it feasible to forecast agricultural output, assess plant health, and improve soil quality. In modern agriculture, robotics, GPS sensors, and drones play significant roles.

eCommerce and retail: Annotating images may improve clients’ user experiences and aid them in selecting the best items. When an annotation is appropriately done, each item on the website will have precise descriptions and labels. Annotation ensures that the products are correctly categorized, improving search results. Additionally, it helps to improve visual search. react to section with heart react to section with light react to section with money react to section with thumbs-down

Conclusion

The goal of image annotation is to make the most of technology, and it is something that will never go away. When key AI projects need image annotation, most AI organizations use expert data labeling services.

react to section with heart react to section with light react to section with money react to section with thumbs-down Businesses that provide expert data labeling services, like Cogito or Anolytics, appen, or any other, have skilled experts to help with data labeling and annotation. These companies can tailor their services to your unique needs as they provide seamless project management, quality control, and lower overheads. Originally published at – https://hackernoon.com/image-annotation-what-no-one-is-talking-about-in-2022

Big data analytics and AI importance in media and entertainment industry

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

What is Data Annotation?

Making the data understandable to machines by labeling using the certain techniques like outlining or shading the text or objects images is basically known as data annotation. The data can be anything from simple text to images or videos and audios available in various formats.

The main motive of data annotation is highlighting the important words, texts or objects using the annotation techniques to make it recognizable to machines or computer vision used for machine learning or artificial intelligence model developments.

Types of Data Annotation

There are different types of data annotation services offered by companies providing machine learning and AI training data. Text Annotation, Image Annotation, Audio Annotation, Video Annotation are the leading types of annotation services you can find in the market. And under each types there are certain annotation techniques like image annotation bounding boxes, cuboid, semantic, polygons and point or polylines are the popular annotation methods.

Types of Data Annotation

Data Annotation for AI and Machine Learning

Apart from few human oriented needs, most of the annotation is done for machine learning and AI data training. Data annotation techniques helps the machines to recognize the actual dimension, shape, size and types of content available on the web based services. Each data has its own format and most of them are understandable to humans but to make such data comprehensible to machines a precise data annotation services is required.

How to Get Annotated Data for Machine Learning or AI?

Developing the machine learning or AI models not only required special skills or knowledge but having a high-quality training data is also important to make such models functional and give the accurate results. Getting the quality training data with right annotation and labeling is difficult, especially if you are looking for free data annotation service.

Cogito is one the well-known companies offers the high-quality machine learning training data with proper annotation and data labeling. It is expert in image annotation and data labeling service for various industries like healthcare, automobiles, retail, agriculture, ecommerce, banking finance and information technology fields with well-diversified clients portfolio.

Which is better for image classification, supervised or unsupervised classification?

Image classification is actually fundamental task that helps to classify and comprehend the entire image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification to images in which only one object appears and that is only analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.

Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools

In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

While on the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.

Training Data for AI and Machine Learning in Insurance Claim

AI in insurance helping to detect the type and level of damage to vehicles. Actually, the AI-based computer vision technology is used to detect the area of damage and calculate the intensity providing an automatic detail for insurance claim processing. Cogito provides, training data for AI and machine learning in insurance with best level of accuracy for right predictions.

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Artificial Intelligence in Urban Management

AI in urban management is becoming possible with well-trained AI system and applications to gather the various details while developing such urban cities. To train the AI models used in urban management a huge quality of training data sets required for machine learning. Cogito provides, the wide ranging image annotation service to annotate the object of interest in the images and making it recognizable to model.     

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How To Use Machine Learning and AI in Inventory Management?

AI in inventory management helping logistic supply chain and warehousing companies to manage the inventory through fully automation system making the whole process fast and more efficient. With training data sets robots and automated machines can be created to develop such AI models. Cogito provides the high quality training data for AI models used in inventory management.

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How is AI Changing Fashion Design or Helping a Clothing Designer?

AI in fashion is playing a crucial role in changing the designs as per the new trend in the market, while keeping in the mind about the current scenario and future prospects. Actually, this technology is equipped with machine learning to understand the historical patterns and buying habits of the customers, and changing the style of fashion with the time being. 

Continue reading “How is AI Changing Fashion Design or Helping a Clothing Designer?”