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Integrated gradients is an explainable AI technique that aims to explain the relationship between a model’s predictions in terms of its features. Adapting this technique to point clouds and semantic segmentation models allows a class-wise attribution of the predictions with respect to the input features. This allows better insight into how a model reached a prediction. Furthermore, it allows a quantitative analysis of how much each feature contributes to a prediction. To obtain these attributions, a baseline with high entropy is generated and interpolated with the point cloud to be visualized. These interpolated point clouds are then run through the network and their gradients are collected. By observing the change in gradients during each iteration an attribution can be found for each input feature. These can then be projected back onto the original point cloud and compared to the predictions and input point cloud. These attributions are generated using RandLA-Net due to it being an efficient semantic segmentation model that uses comparatively few parameters, therefore keeping the number of gradients that must be stored at a reasonable level. The attribution was run on the public Semantic3D dataset and the SVGEO large-scale urban dataset.

This presentation was delivered at the 3rd Annual BMES/FDA Frontiers in Medical Devices Conference. This presentation covers three key topics:1. Reporting Computational Modeling Studies in Medical Device Submissions, FDA Guidance2. Assessing Credibility of Computational Modeling through Verification and Validation, ASME V&V40 Draft Standard3. Critical Thinking

This presentation was delivered at the 3rd Annual BMES/FDA Frontiers in Medical Devices Conference. This presentation covers three key topics:1. Reporting Computational Modeling Studies in Medical Device Submissions2. Assessing Credibility of Computational Modeling through Verification and Validation3. Critical Thinking

This presentation was delivered at the 3rd Annual BMES/FDA Frontiers in Medical Devices Conference. This presentation covers three key topics:1. Reporting Computational Modeling Studies in Medical Device Submissions, FDA Guidance2. Assessing Credibility of Computational Modeling through Verification and Validation, ASME V&V40 Draft Standard3. Critical Thinking

In this article, which is assembled from interviews, the main issues of in silicomedicine, present and future, are discussed by three scientists who are directly involved in the implementation and development of in silicotechniques