Building Computer Vision Models for Insurance with Tractable

Introducing Customer: Voxel Voxel is a global leader in workplace safety, empowering worksites by providing them with the data they need to protect workers and gain insight into workplace activities. Their mission is to protect the people who power our world. We spoke with Anurag Kanungo, the co-founder and CTO, about why he decided to transition to Encord to manage their machine learning pipeline and computer vision projects. Problem: Operational Challenges in Data Accessibility and Model Scalability As Voxel grew, they encountered several challenges that hampered their ability to deliver on their mission effectively. The initial approach to data gathering and analysis wasn't sufficient for scale, leading to difficulties in finding relevant data and a lack of dataset diversity. The frequent changes in work environments, such as uniform updates, posed challenges in accurately updating models with new, unseen data. Also, addressing model edge cases and efficiently scaling the data labeling and analysis process became a prominent issue. Initially, Voxel trained pipelines using open-sourced tools like CVAT for object detection in videos. While sufficient on a small scale, as Voxel grew and required more complexity, the limitations of these tools became evident. Among others, they faced challenges with the user interface, backend data management, interpolation issues, and label exports. Despite being a good starting point, these tools proved inadequate for scaling operations effectively. “…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like CVAT kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.” - Anurag Kanungo   As Voxel scaled, they sought a more robust solution that had critical features such as video support and image classification. Solution: Transitioning to Encord for Scalable and Efficient Video Analysis The decision to transition to Encord marked a significant turning point for Voxel. Encord's video-first approach addressed their need for robust video support, while its innovative features, such as image group classification, stood out. Moreover, Encord's exceptional support and technical design resonated with Voxel's needs, offering a seamless and efficient solution that aligned perfectly with their vision for enhancing workplace safety. "We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios."- Anurag Kanungo   Results: Impact of Encord on Voxel’s Operations One of the key requirements for Voxel was the ability to integrate their existing data pipelines into a new solution, which Encord was able to provide seamlessly. This enabled their team to continue to focus on their end solution without being preoccupied with the handover.  Voxel were impressed by the robustness of the platform, enabling them to utilise many of the advanced features enabling them to address the safety issues and ergonomic concerns more effectively, aligning with their overarching mission to reduce workplace risks and ensure a safer environment for all workers Overall, the adoption of Encord has significantly aided Voxel's approach to workplace safety and efficiency. The platform's integration and its capabilities have empowered Voxel to address safety concerns and optimize operations effectively. With Encord's ongoing support, Voxel is well-equipped to navigate future challenges and drive innovation in workplace safety, setting new standards for operational excellence.

CONXAI is building an AI platform for the Architecture, Engineering and Construction industries to contextualise different data and transform them into actionable insights. CONXAI, however, encountered challenges with optimizing datasets, reviewing labels, and managing large volumes of data with their in-house data annotation solution. This is where Encord came in - CONXAI was looking for an end-to-end solution for data management and curation, annotation, and evaluation. Introducing Customer: CONXAI CONXAI’s goal is to help AEC teams perform better by organizing and making sense of the vast amount of data generated during different stages of construction projects. They specialize in making data more useful, especially since a lot of project data often goes unused. Their ultimate aim is to help AEC professionals use AI effectively to improve efficiency and tackle challenges in their projects. We sat down with Markus Kittel, AI Product Development Manager at CONXAI, to discuss his work overseeing the product roadmap, and their exciting plans ahead for the business. Problem: Challenges in Data Curation and Management CONXAI's approach involves working with large unstructured datasets, which leads to challenges in effectively managing and curating project data. Their initial reliance on their in-house solution for data annotation proved to be problematic as the volume of data increased. Like many in-house tools, it was prone to frequent malfunctions, obscured the data it processed, and lacked mechanisms for reviewing annotations. Additionally, scalability was a major concern, as the in-house tool struggled to handle the increasing volume and complexity of project data. Without a reliable and scalable data management system in place, they faced difficulties in optimizing datasets and analyzing data effectively. As a result, CONXAI recognized the pressing need for a comprehensive solution that could streamline its data curation and management processes, enabling it to unlock the full potential of AI-driven insights within the AEC industry. CONXAI were also in need of a solution where data security took precedence, enabling data to remain within CONXAI servers and be accessed via an API or SDK. Solution: Encord Provides a Unified Platform for Data Curation and Management “With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,” Markus says. To address these challenges, CONXAI explored various annotation tools. They were searching for a single platform that could handle data curation and management seamlessly. Encord's Annotate and Encord Active emerged as the ideal solution, offering a comprehensive platform to streamline CONXAI’s operations. As Markus says “We connect Encord Active with our large dataset and then use metrics to prioritize building a collection of images. This collection is then sent to Encord Annotate for labeling images in preparation for training. And all this without the data leaving our server.” Result: 60% Increase in Labeling Speed With the adoption of Encord into the data pipeline, CONXAI witnessed significant improvements in its data management processes. Encord facilitated the transformation of unstructured data into labeled, training-ready, datasets. The intuitive interface of Encord's Annotate tool simplified the annotation process for CONXAI's team, while also providing robust label review capabilities. Moreover, Encord's Active platform allowed CONXAI to efficiently curate and evaluate their datasets. “The labeling speed of the annotation team improved to almost 60% compared to when using their in-house tool.” - Markus Kittel  CONXAI was able to curate over 40k images with Encord Active. They were then able to efficiently evaluate and prioritize these images based on metrics, facilitating streamlined data management and enhanced decision-making processes within their operations. CONXAI were able to contribute to Encord’s product roadmap by identifying that mapping relationships between labels in their ontology would enhance model performance. The Encord team were able to deploy this functionality, resulting in an improved user experience for CONXAI. Overall, using Encord led to enhanced robustness, simplified data pipelines, and a remarkable 60% increase in labeling speed compared to CONXAI's previous in-house tool. This demonstrates how adopting an end-to-end platform with annotation, curation, and evaluation capabilities provides the best solution for computer vision teams. 

Introduction A new paper published in MDPI (Multidisciplinary Digital Publishing Institute) demonstrates how, using the Encord platform, researchers at Harvard Medical School, Massachusetts General Hospital, and Brigham and Women’s Hospital were able to reduce vascular ultrasound annotation time from days to minutes and run automated analyses of their datasets. Using Encord, the team was able to: Create their first segmentation models by labeling only a handful of images Cut annotation time through segmentation models by an order of magnitude Visually explore their dataset and identify problematic areas - in their case, the impact of blur on their dataset Evaluate the performance of their segmentation models in the Encord platform  Problem: DUS Image Annotation is Resource-Intensive and Prone to Human Error Medical imaging, particularly Arterial Duplex Ultrasound (DUS), plays a crucial role in diagnosing and managing vascular diseases like Popliteal Artery Aneurysms (PAAs). The traditional method of analyzing DUS images relies heavily on manual annotation by skilled medical professionals. This process is fraught with challenges: Time-consuming—especially with the growing volume of medical imaging data. Prone to human error. Heavily dependent on expertise and experience - furthering how resource-intensive the process becomes The subjective nature of manual annotations can lead to inconsistent measurements and interpretations due to inter- and intra-observer variability during annotation. This raises concerns about the reliability and reproducibility of the results and could impact the accuracy of diagnoses and treatment plans for patients. The primary issue in this research paper lies in precisely annotating the inner and outer lumens of the artery in images - a critical step for accurate measurement and subsequent treatment planning. Solution: Encord Annotate to Auto-Label DUS Images and Encord Active to Validate Model Performance The study tested the feasibility of the Encord platform to create an automated model that segments the inner and outer lumen within PAA DUS. Using image segmentation to find the largest diameter and thrombus area within PAAs helped standardize DUS measurements that are important for making decisions about surgery.  Using Encord Annotate for Automated Annotation The researchers collected and prepared (deidentification and extraction) a dataset comprising DUS images of PAAs for upload to Encord before annotating a few images to serve as ground truth for the annotation models using Encord Annotate. Using Encord Annotate’s automated labeling feature, they could generate segmentation masks for unlabeled images. This reduced the time and effort required for DUS image analysis while minimizing the potential for human error.  Using Encord Active to Select the Best-Performing Model They trained three models and validated them with Encord Active on the annotated images (20, 60, and 80 sets). Encord Active enabled the researchers to understand the performance metrics that helped them select the best model for segmenting the inner and outer lumens of the popliteal artery with high precision.  After training models on image subsets, we tested them within the Encord platform. We selected the desired tests in the analysis tab of the project, and after a runtime period, the platform presented calculations of true positives, false negatives, mAP, IoU, and blur. The report referenced Encord’s ability to seamlessly integrate into clinical processes with a user-friendly interface, simple onboarding, and rapid annotation workflows as crucial to the study's success. For healthcare practitioners who use the platform, this improves their diagnostic process without disrupting established procedures. Results Encord Reduced Annotation Time from Days to Minutes Where manual annotation could take several minutes per image, the researchers accomplished the task in a fraction of the time using Encord. Their workflow went from relying on RPVI-certified physicians manually annotating DUS images that took days to use Encord to annotate a few images, train models, and auto-label unlabeled images in minutes.  This efficiency proves crucial in clinical settings, where timely diagnosis and treatment decisions can significantly impact patient outcomes. Figure 1. AI segmentation classifications on duplex ultrasound images. (A) Outer polygon true-positive classification, where the color green indicates a correct segmentation. (B) Outer polygon false-positive classification, where red indicates an incorrect segmentation. (C) Inner polygon true-positive classification, where the color green indicates a correct segmentation. (D) Inner polygon false-positive classification, where red indicates an incorrect segmentation. Better Evaluation and Observability of Model Performance with Encord Active The researchers quantitatively assessed the performance of the three models with Encord Active providing analytics on the following metrics:  mean Average Precision (mAP).  Intersection over Union (IoU). True Positive Rate (TPR). Encord Active calculated the outer polygon's mAP to be 0.85 for the 20-image model, 0.06 for the 60-image model, and 0 for the 80-image model. The mAP of the inner polygon was 0.23 for the 20-image, 60-image, and 80-image models. The true-positive rate (TPR) for the inner polygon remained at 0.23. See the full results in the table below: “With regard to the models for outer and inner polygons, the outer polygon model outperformed the inner polygon model on every metric. The outer polygon demonstrated almost equal precision and recall at 0.85. The mAP for the outer polygon model was 0.85 with a true-positive rate of 0.86, which is comparable to other clinically used high-performing models for US segmentation.” With Encord Active automatically providing model evaluation analytics, the team instantly discovered the model's strengths and weaknesses. For every model they trained, Active provided breakdowns and graphs on its performance, including the ability to visually explore the regions the model incorrectly segmented vs. the ground truth. Encord Active Uncovered Blurry DUS Images that Could Degrade Annotation Model Performance The researchers used Encord Active to explore the model's performance depending on the blur level, allowing them to visually interact with varying levels of blur in their dataset to understand how this impacted model performance. The paper states, “Intuitively, our analysis found that as the images became blurrier, the model precision declined, and false-negative rates increased... Removing blur from—or augmenting—blur in images can be important for training accurate AI models.” Conclusion In summary, the platform’s intuitive navigation, complemented by tutorials for both model training and analysis, allowed for straightforward operationalization of the model training system among members of the research team. The results were displayed in an understandable format and interpreted within the following discussion. The findings have far-reaching consequences for medical imaging and diagnosis. The researchers greatly improved the accuracy, reliability, and efficiency of DUS image analysis by auto-annotating images with Encord Annotate and validating annotation models with Encord Active. This could result in potentially better patient care, treatment planning, and diagnostic procedures. At Encord, we are committed to continually providing healthcare practitioners and physicians with the data-centric AI platform they need to improve their medical imaging and analysis workflows.  We’re proud of the work the researchers were able to accomplish and how Encord is paving the way for broader applications of AI in various aspects of medical diagnostics.   📑 Read the full paper on MDPI (Multidisciplinary Digital Publishing Institute).