ShapeAXI: shape analysis explainability and interpretability

Juan Carlos Prieto; Felicia Miranda; Marcela Gurgel; Luc Anchling; Nathan Hutin; Selene Barone; Najla Al Turkestani; Aron Aliaga; Marilia Yatabe; Jonas Bianchi; Lucia Cevidanes

doi:10.1117/12.3007053

2 April 2024 ShapeAXI: shape analysis explainability and interpretability

Juan Carlos Prieto, Felicia Miranda, Marcela Gurgel, Luc Anchling, Nathan Hutin, Selene Barone, Najla Al Turkestani, Aron Aliaga, Marilia Yatabe, Jonas Bianchi, Lucia Cevidanes

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293116 (2024) https://doi.org/10.1117/12.3007053
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

ShapeAXI represents a cutting-edge framework for shape analysis that leverages a multi-view approach, capturing 3D objects from diverse viewpoints and subsequently analyzing them via 2D Convolutional Neural Networks (CNNs). We implement an automatic N-fold cross-validation process and aggregate the results across all folds. This ensures insightful explainability heat-maps for each class across every shape, enhancing interpretability and contributing to a more nuanced understanding of the underlying phenomena. We demonstrate the versatility of ShapeAXI through two targeted classification experiments. The first experiment categorizes condyles into healthy and degenerative states. The second, more intricate experiment, engages with shapes extracted from CBCT scans of cleft patients, efficiently classifying them into four severity classes. This innovative application not only aligns with existing medical research but also opens new avenues for specialized cleft patient analysis, holding considerable promise for both scientific exploration and clinical practice. The rich insights derived from ShapeAXI’s explainability images reinforce existing knowledge and provide a platform for fresh discovery in the fields of condyle assessment and cleft patient severity classification. As a versatile and interpretative tool, ShapeAXI sets a new benchmark in 3D object interpretation and classification, and its groundbreaking approach hopes to make significant contributions to research and practical applications across various domains. ShapeAXI is available in our GitHub repository https://github.com/DCBIA-OrthoLab/ShapeAXI.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juan Carlos Prieto, Felicia Miranda, Marcela Gurgel, Luc Anchling, Nathan Hutin, Selene Barone, Najla Al Turkestani, Aron Aliaga, Marilia Yatabe, Jonas Bianchi, and Lucia Cevidanes "ShapeAXI: shape analysis explainability and interpretability", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293116 (2 April 2024); https://doi.org/10.1117/12.3007053

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