Improving Efficiency in the Construction Industry with Encord

Introducing Customer: Neurons  Neurons has been a leading company in the field of neuroscience marketing for more than 10 years. With their expertise in understanding how humans react to content, they have developed AI-powered solutions to enable companies to create predictable marketing content at scale. Their mission is to eliminate biases and guesswork from marketing strategies. As a pioneer in this domain, Neurons faced significant challenges in their data annotation process due to the sheer volume of label categories and the growing number of assets requiring annotation. We sat down with Dennis Green-Lieber (Director of Product and Engineering) and Konstantina Kaisheva (Innovation project manager) to discuss how Encord alleviated these challenges. Problem: Challenges with in-house solution Neurons, historically, have relied on manual annotation by their internal team using their in-house annotation platform. With a growing volume of assets requiring annotation, they found it increasingly challenging to scale their operations using their in-house solutions. They also encountered difficulties in ensuring precision and consistency when labeling large volumes of data, resulting in delays in getting their models to production. Solution: Scaling with Encord's end-to-end platform Seeking a solution to their data annotation challenges, Neurons turned to Encord’s platform. They recognized the need for manual and automated labeling capabilities, so they were drawn to Encord’s comprehensive offering. With Encord, Neurons found a user-friendly platform that facilitated a precise and efficient annotation process. They appreciated the ability to define labeling instructions/ontologies tailored to their specific requirements, ensuring accuracy in their data sets. Encord's platform offered features for quality assurance, workflow management, and data categorization streamlining Neurons’ annotation workflows. They were particularly impressed by Encord's flexibility, which allowed them to adapt labeling criteria for different content types. Konstantina, the R&D manager of the AI team, noted her happiness with the function of model evaluations. Recognizing its potential as an invaluable tool for the future, she highlighted its ability to provide insights into the performance and accuracy of its AI models. "It's a very easy platform to operate. What I really liked was the whole offering of Encord for both automated labeling and manual work, as precision is incredibly important to us."- Konstantina Kaisheva   Result: Efficient and fast annotation With Encord’s platform, Neurons achieved marked improvements in their data annotation process. With the appropriate workflows and quality assurance, they were able to maintain precision and consistency even when they were handling larger volumes of data. This led to building a higher-quality dataset for their AI models.  Neuron’s newly launched Copilot, provides actionable insights for marketing decision makers. This is run by AI models which require large volumes of precisely annotated datasets, which Encord enabled them to achieve. By eliminating guesswork and biases, they empowered their clients to make informed decisions and optimize their marketing strategies effectively. Overall, Encord’s platform proved to be a significant asset for Neurons, enabling them to overcome their data annotation challenges. With a newfound ability to make data-driven decisions, the company saw a tangible improvement in its ability to achieve their desired model outcomes.

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.

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).