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This Raksha Bandhan, we at Objectways Technologies celebrate the unbreakable bond between brothers and sisters. Just like a well-annotated dataset protects and empowers AI models, a brother's promise protects and empowers his sister.

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The healthcare industry has witnessed the remarkable growth of artificial intelligence (AI), which has found diverse applications. As technology advances, AI’s potential in healthcare continues to expand. Nevertheless, certain limitations currently hinder the seamless integration of AI into existing healthcare systems. 

AI is used in healthcare datasets to analyze data, provide clinical decision support, detect diseases, personalize treatment, monitor health, and aid in drug discovery. It enhances patient care, improves outcomes, and drives advancements in the healthcare industry. Many AI services such as Amazon Comprehend Medical, Google Cloud Healthcare API, John Snow Labs provide pre-built models. Due to the variety of medical data and requirements for accuracy human in the loop techniques are important to safeguard accuracy. However, the success of AI and ML models largely depends on the quality of the data they are trained on, necessitating reliable and accurate data labelling services. 

Challenges in applying AI in Healthcare 

Extensive testing of AI is necessary to prevent diagnostic errors, which account for a significant portion of medical errors and result in numerous deaths each year. While AI shows promise for accurate diagnostics, concerns remain regarding potential mistakes. Ensuring representative training data and effective model generalization are crucial for successful AI integration in healthcare. 

In the healthcare sector, ensuring the privacy and security of patient data is paramount, as it not only fosters trust between healthcare providers and patients, but also complies with stringent regulatory standards such as HIPAA, promoting ethical, responsible data handling practices. 

Lack of High-Quality Labeled datasets 

Achieving a high quality labeled medical dataset poses several challenges, including: 

Medical Labeling Skills: -Properly labeling medical data requires specialized domain knowledge and expertise. Medical professionals or trained annotators with a deep understanding of medical terminology and concepts are necessary to ensure accurate and meaningful annotations. 

Managing Labeling Quality: -Maintaining high-quality labeling is crucial for reliable and trustworthy datasets. Ensuring consistency, accuracy, and minimizing annotation errors is challenging, as medical data can be complex and subject to interpretation. Robust quality control measures, including double-checking annotations and inter-annotator agreement, are necessary to mitigate labeling inconsistencies. 

Managing the Cost of Labeling: -Labeling medical datasets can be a resource-intensive process, both in terms of time and cost. Acquiring sufficient labeled data may require significant financial investment, especially when specialized expertise is involved. Efficient labeling workflows, leveraging automation when feasible, can help manage throughput and reduce costs without compromising data quality. 

Data Privacy and Security: -Safeguarding patient privacy and ensuring secure handling of sensitive medical data is crucial when collecting and labeling datasets. 

Data Diversity and Representativeness: -Ensuring that the dataset captures the diversity of medical conditions, demographics, and healthcare settings is essential for building robust and unbiased AI models. 

Best practices to manage medical labeling projects 

Addressing these challenges requires a combination of domain expertise, quality control measures, and optimizing labeling processes to strike a balance between accuracy, cost-effectiveness, and dataset scale. 

At Objectways we follow the Best Practices in medical labeling which include 

Adherence to Guidelines: -Familiarize labeling teams with clear and comprehensive guidelines specific to the medical domain. Thoroughly understanding the guidelines ensures consistent and accurate labeling. 

Conducting KPT (Knowledge, Process, Test): -Provide comprehensive training to labeling teams on medical concepts, terminology, and labeling procedures. Regular knowledge assessments and testing help evaluate proficiency of labeling teams and ensure continuous improvement. 

Robust Team Structure: -We have established a structured team comprising labeling personnel, spot Quality Assurance (QA) reviewers, and dedicated QA professionals. This structure promotes accountability, efficient workflow, and consistent quality. 

Quality Metrics: -We have implemented appropriate quality metrics such as precision, recall, and F1 score to assess labeling accuracy. We regularly monitor and track these metrics to identify areas for improvement and maintain high-quality standards. 

Continuous Feedback Loop: -We have established a feedback mechanism where labeling teams receive regular feedback on their performance. This helps address any inconsistencies, clarify guidelines, and improve overall labeling accuracy. 

Quality Control and Spot QA: - By implementing robust quality control measures, including periodic spot QA reviews by experienced reviewers, helps identify and rectify any labeling errors, ensures adherence to guidelines, and maintains high labeling quality. 

Data Security and Privacy: - To validate our commitment to security and privacy controls, we have obtained the following formal certifications SOC2 Type2, ISO 27001, HIPAA, and GDPR. These certifications affirm our dedication to safeguarding customer data. Our privacy and security programs continue to expand, adhering to Privacy by Design principles and incorporating industry standards and customer requirements from various sectors. 

Summary 

At Objectways we have a team of certified annotators, including medical professionals such as nurses, doctors, and medical coders. Our experience includes working with top Cloud Medical AI providers, Healthcare providers and Insurance companies, utilizing advanced NLP techniques to create top-notch training sets and conduct human reviews of pre-labels across a wide variety of document formats and ontologies, such as call transcripts, patient notes, and ICD documents. Our DICOM data labeling services for computer vision cover precise annotation of medical images, including CT scans, MRIs, and X-rays and expert domain knowledge in radiology to ensure the accuracy and quality of labeled data. 

ChatGPT has made AI a sensation among all enterprises and the everyday common man. It surpassed the user base to 1M within a week and broke many records. Even Bill gates' compared the success of ChatGPT to the most important milestones such as the birth of the internet, personal computers, and GUI. “This will change our world,” he continued. The applications of generative AI like OpenAI’s ChatGPT could improve office efficiency, drafting invoices and letters, Gates said in a podcast conversation with the German-language business paper. 

Large Language Models (LLMs) are becoming increasingly popular for natural language processing (NLP) tasks, such as language translation, text summarization, and text generation. While LLMs have shown remarkable performance in these tasks, they are not without their limitations and potential drawbacks. LLMs are often trained on large datasets of text, which may contain biased or sensitive information. If these biases are not corrected, the LLM may perpetuate or amplify them in its language generation. Human reviewers are needed to evaluate the quality and appropriateness of LLM-generated text and to correct any errors or biases that are identified. LLMs are not yet capable of understanding the nuances and complexities of human language in the same way that humans can. Human reviewers are needed to ensure that LLM-generated text is understandable, coherent, and consistent with the intended meaning. 

What is LLM Reinforcement Learning Human Feedback (RLHF) 

LLM reinforcement learning human feedback (LLM RHLF) refers to a method of training large language models (LLMs) using a combination of reinforcement learning and human feedback. Reinforcement learning is a type of machine learning where an algorithm learns to make decisions by trial and error. In the context of LLMs, reinforcement learning can be used to optimize the performance of the LLM by providing it with feedback on the quality of its generated text. 

Human feedback, on the other hand, is provided by human reviewers who evaluate the quality and appropriateness of LLM-generated text. Human feedback can be used to correct any errors or biases in the LLM-generated text and to improve the overall performance of the LLM. 

LLM RHLF combines these two approaches by training the LLM using reinforcement learning and then using human feedback to evaluate and improve the quality of the LLM-generated text. This approach can help to improve the accuracy, quality, and appropriateness of LLM-generated text and to reduce any biases or errors that may be present in the training data. 

How LLMs are augmented by Human Reviewers 

There are several typical human review tasks for Large Language Models (LLMs): 

Bias Evaluation: -One of the main concerns with LLMs is their potential to perpetuate or amplify biases present in the training data. Human reviewers can evaluate LLM-generated text for any biases or stereotypes that may have been introduced. 

Accuracy Checking: -LLMs are not always accurate in their language generation, especially when faced with complex or nuanced language. Human reviewers can check LLM-generated text for accuracy and correct any errors that are identified. 

Style and Tone Evaluation: -LLMs may not always generate text that is appropriate for the intended audience or purpose. Human reviewers can evaluate LLM-generated text for style, tone, and appropriateness. 

Contextual Evaluation: -LLM-generated text may not always be suitable for a given context or situation. Human reviewers can evaluate LLM-generated text for context and ensure that it is appropriate for its intended use. 

Content Verification: - LLMs may generate text that contains false or misleading information. Human reviewers can verify the accuracy of LLM-generated content and correct any errors that are identified. 

Human Reviewers’ Skills 

Human reviewers for Large Language Models (LLMs) need to have specific skills and experience to effectively evaluate and correct LLM-generated text. Here are some of the key skills and experience needed: 

Language Skills: - Human reviewers need to have a strong command of the language(s) in which the LLM is generating text. They need to be able to understand the nuances of the language and ensure that LLM-generated text is grammatically correct, coherent, and appropriate for its intended audience and purpose. 

Subject Matter Expertise: - Depending on the domain for which the LLM is generating text, human reviewers may need to have subject matter expertise in that area. For example, human reviewers working on LLM-generated medical text may need to have a background in medicine or healthcare. 

Analytical Skills: -Human reviewers need to be able to critically evaluate LLM-generated text for accuracy, bias, style, tone, and context. They need to be able to identify errors and biases and correct them as needed. 

Attention to Detail: - Human reviewers need to be detail-oriented and meticulous in their review of LLM-generated text. They need to be able to identify even small errors or inconsistencies and correct them as needed. 

Cultural Competence: - Human reviewers need to be aware of cultural differences and sensitivities in the language(s) for which the LLM is generating text. They need to ensure that LLM-generated text is appropriate for its intended audience and does not offend or marginalize any group.    Overall, human reviewers for LLM tasks need to have a combination of language skills, subject matter expertise, analytical skills, attention to detail, and cultural competence. They play a critical role in ensuring the accuracy, quality, and appropriateness of LLM-generated text, and their skills and experience are essential to the success of LLM-based language processing 

Summary 

User-generated content (UGC) can be a valuable asset for digital platforms and businesses, providing an opportunity for users to engage with content and each other. However, UGC can also present several challenges and issues, including: 

Legal and regulatory compliance: -UGC can sometimes infringe on legal and regulatory requirements, such as copyright, privacy, or advertising standards. Digital platforms and businesses need to ensure that the UGC posted by users is compliant with these requirements to avoid legal or regulatory repercussions. 

Offensive or harmful content: -UGC can include content that is offensive, hateful, or harmful, such as hate speech, discriminatory language, or graphic violence. This type of content can lead to a negative user experience and may even damage brand reputation. 

Inappropriate content: -UGC can also include inappropriate or irrelevant content, such as spam, fake reviews, or irrelevant comments. This can affect the overall quality of the content and make it difficult for users to find relevant and useful information. 

Misinformation: -UGC can also include false or misleading information, which can be harmful to users and to the platform or business. Digital platforms and businesses must take steps to ensure that UGC is accurate and truthful. 

Brand safety: -UGC can also pose a risk to brand safety, particularly if the content includes references to competitors or is critical of the platform or business. Digital platforms and businesses need to monitor UGC to ensure that it is not damaging to their brand image. 

Moderation cost and scalability: -UGC can be difficult and expensive to moderate at scale. Digital platforms and businesses may need to invest in moderation tools, processes, and personnel to ensure that UGC is reviewed and moderated effectively. 

Overall, UGC presents a number of challenges and issues that must be addressed by digital platforms and businesses. By implementing appropriate moderation processes, tools, and guidelines, these challenges can be mitigated, and UGC can continue to provide value to both users and the platform or business. 

Best Practices to Manage Content Moderation Processes 

Best Practices to Manage Content Moderation Processes Content moderation best practices involve a combination of human and automated moderation techniques, clear and transparent policies, and effective communication with users. By implementing these practices, digital platforms and businesses can provide a safe and positive user experience while complying with legal and regulatory requirements. 

Objectways Content Moderation Human Review workflows involve following best practices: 

User report or flagging: -The first step in the human content moderation workflow is for users to report or flag content that they believe violates platform policies or community guidelines. 

Review content: -Once the content is flagged, it is then reviewed by a human content moderator. The moderator will evaluate the content to determine if it violates any platform policies or community guidelines. 

Categorize the content: -The moderator will categorize the content based on the type of violation, such as hate speech, graphic violence, or adult content. 

Take action: -The moderator will then take appropriate action depending on the type of violation. This can include removing the content, warning the user, or escalating the issue to a higher authority. 

Appeal process: -Platforms often provide an appeal process for users to dispute moderation decisions. If a user appeals, the content will be reviewed by a different moderator or team. 

Monitoring and continuous improvement: - After content is moderated, it is important to monitor the platform for repeat offenses or new types of violations. This helps to identify new patterns and improve the moderation process. 

Choose the Right Moderation Tool 

There are many tools available to help digital platforms and businesses with content moderation. Here are some leading tools for content moderation: 

Google Perspective API: -Google Perspective API uses machine learning to identify and score the level of toxicity in a piece of content, making it easier for moderators to identify content that may violate community guidelines. 

Amazon Rekognition:-Amazon Rekognition is a powerful image and video analysis tool that can be used to automatically identify and moderate content that violates community guidelines. 

OpenAI GPT-3:-OpenAI GPT-3 is a natural language processing tool that can be used to analyze and moderate user-generated content, such as comments and reviews. 

Besedo:-Besedo is a content moderation platform that uses a combination of AI and human moderators to review and moderate user-generated content. 

We typically work with tools chosen by our customer partners or use Amazon Rekognition for our Turnkey Content Moderation offering as it is easy to use, provides nice integration and human in the loop capabilities. 

Quality Service Level Agreements 

Service Level Agreements (SLAs) are agreements between our customers who are digital platforms, businesses and Objectways content moderation team. The SLAs specify the expected quality and quantity of content moderation services and set out the penalties for failure to meet those expectations. Our SLAS can include: 

Turnaround time: -Google Perspective API uses machine learning to identify and score the level of toxicity in a piece of content, making it easier for moderators to identify content that may violate community guidelines. 

Accuracy rate: -Amazon Rekognition is a powerful image and video analysis tool that can be used to automatically identify and moderate content that violates community guidelines. 

Rejection rate: -OpenAI GPT-3 is a natural language processing tool that can be used to analyze and moderate user-generated content, such as comments and reviews. 

Escalation process: -Besedo is a content moderation platform that uses a combination of AI and human moderators to review and moderate user-generated content. 

Availability: -This specifies the availability of content moderation services, such as 24/7 or during specific business hours. 

Reporting and communication: -This specifies the frequency and format of reporting on content moderation activities, as well as the communication channels for raising issues or concerns. 

Penalties and incentives: -This specifies the penalties or incentives for failing to meet or exceeding the SLA targets, such as financial penalties or bonuses. 

By establishing clear SLAs for human content moderation, digital platforms and businesses can ensure that the quality and quantity of content moderation services meet their requirements and standards. It also allows them to hold our content moderation teams accountable for meeting performance targets and provide transparency to their users. 

Conclusion 

In conclusion, content moderation human review plays a critical role in maintaining the safety, security, and trustworthiness of online platforms and communities. By providing a human layer of review and oversight, content moderation helps to ensure that inappropriate or harmful content is removed, while legitimate and valuable content is allowed to thrive. 

However, content moderation human review is a complex and challenging process, requiring careful planning, organization, and management. It also requires a deep understanding of the cultural, linguistic, and social nuances of the communities being moderated, as well as a commitment to transparency, fairness, and accountability. 

By leveraging best practices, advanced technologies, and human expertise, it is possible to create effective and efficient content moderation human review workflows that support the goals and values of online platforms and communities. Whether it’s combating hate speech, preventing cyberbullying, or ensuring the safety of children, content moderation human review is a critical tool in creating a safer and more trustworthy online world for everyone. 

Deep learning has been very successful when working with images as data and is currently at a stage where it works better than humans on multiple use-cases. The most important problems that humans have been interested in solving with computer vision are image classification, object detection and segmentation in the increasing order of their difficulty. 

While there in the plain old task of image classification we are just interested in getting the labels of all the objects that are present in an image. In object detection we come further and try to know along with what all objects that are present in an image, the location at which the objects are present with the help of bounding boxes. Image segmentation takes it to a new level by trying to find out accurately the exact boundary of the objects in the image. 

What is image segmentation? 

We know an image is nothing but a collection of pixels. Image segmentation is the process of classifying each pixel in an image belonging to a certain class and hence can be thought of as a classification problem per pixel. There are two types of segmentation techniques 

segmentation: - Semantic segmentation is the process of classifying each pixel belonging to a particular label. It doesn’t different across different instances of the same object. For example, if there are 2 cats in an image, semantic segmentation gives same label to all the pixels of both cats 

Instance segmentation: - Instance segmentation differs from semantic segmentation in the sense that it gives a unique label to every instance of a particular object in the image. As can be seen in the image above all 3 dogs are assigned different colors i.e different labels. With semantic segmentation all of them would have been assigned the same color. 

There are numerous advances in Segmentation algorithms and open-source datasets. But to solve a particular problem in your domain, you will still need human labeled images or human based verification. In this article, we will go through some of the nuances in segmentation task labeling and how human based workforce can work in tandem with machine learning based approaches. 

To train your machine learning model, you need high quality labels. For a successful data labeling project for segmentation depends on three key ingredients. 

Labeling Tools 

Training 

Quality Management 

Labeling Tools 

There are many open source and commercially available tools on the market. At objectways, we train our workforce using Open CVAT that provides a polygon tool with interpolation and assistive tooling that gives 4x better speed at labeling and then we use a tool that fits the use case. 

Here are the leading tools that we recommend for labeling. For efficient labeling, prefer a tool that allows pre-labeling and assistive labeling using techniques like Deep Extreme Cut or Grab cut and good review capabilities such as per label opacity controls. 

Workforce training 

While it is easier to train a resource to perform simple image tasks such as classification or bounding boxes, segmentation tasks require more training as it involves multiple mechanisms to optimize time, increase efficiency and reduce worker fatigue. Here are some simple training techniques 

Utilize Assistive Tooling: An annotator may start with a simple brush or polygon tool which they find easy to pick up. But at volume, these tools tend to induce muscle fatigue hence it is important to make use of assistive tooling. 

Gradually introduce complex tasks: Annotators are always good at doing the same task more efficiently with time and should be part of the training program. At Objectways, we tend to start training by introducing annotators with simple images with relatively easy shapes (Cars/Buses/Roads) and migrate them to using complex shapes such as vegetation, barriers. 

Use variety of available open-source pre-labeled datasets: It is also important to train the workforce using different datasets and we use PascalVoc, Coco, Cityscapes, Lits, CCP, Pratheepan, Inria Aerial Image Labeling. 

Provide Feedback: It is also important to provide timely feedback about their work and hence we use the golden set technique that is created by our senior annotators with 99.99% accuracy and use it to provide feedback for annotators during the training. 

Quality Management 

In Machine Learning, there are different techniques to understand and evaluate the results of a model. 

Pixel accuracy: Pixel accuracy is the most basic metric which can be used to validate the results. Accuracy is obtained by taking the ratio of correctly classified pixels w.r.t total pixels. 

Intersection over Union: IOU is defined as the ratio of intersection of ground truth and predicted segmentation outputs over their union. If we are calculating for multiple classes, the IOU of each class is calculated, and their meaning is taken. It is a better metric compared to pixel accuracy as if every pixel is given as background in a 2-class input the IOU value is (90/100+0/100)/2 i.e 45% IOU which gives a better representation as compared to 90% accuracy. 

F1 Score: The metric popularly used in classification F1 Score can be used for segmentation tasks as well to deal with class imbalance. 

If you have a labeled dataset, you can introduce a golden set in the labeling pipeline and use one of the scores to compare labels against your own ground truth. We focus on following aspects to improve quality of labeling 

Understand labeling instructions: Never underestimate the importance of good labeling instructions. Typically, instructions are authored by data scientists who are good at expressing what they want with examples. The human brain has a natural tendency to give weight to (and remember) negative experiences or interactions more than positive ones — they stand out more. So, it is important to provide bad labeling examples. Reading instructions carefully often weeds out many systemic errors across tasks. 

Provide timely feedback: While many workforces use tiered skilled workforce where level1 workforce are less experienced than quality control team, it is important to provide timely feedback to level1 annotators, so they understand unintentional labeling errors, so they do not make those errors in the future tasks 

Rigorous Quality audits: Many tools provide nice metrics to track label addition/deletion or change over time. Just as algorithms should converge and reduce the loss function, the time to QC a particular task and suggested changes should converge to less than .01% error rate. At objectways, we have dedicated QC and super QC teams who have a consistent track record to achieve over 99% accuracy. 

Summary