Gotkowski, Karol ; Gonzalez, Camila ; Bucher, Andreas ; Mukhopadhyay, Anirban (2021)
M3d-CAM: a PyTorch Library to Generate 3D Attention Maps for Medical Deep Learning.
German Workshop on Medical Image Computing. Regensburg, Germany (07.03.2021-09.03.2021)
doi: 10.1007/978-3-658-33198-6_52
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Deep learning models achieve state-of-the-art results in a wide array of medical imaging problems. Yet the lack of interpretability of deep neural networks is a primary concern for medical practitioners and poses a considerable barrier before the deployment of such models in clinical practice. Several techniques have been developed for visualizing the decision process of DNNs. However, few implementations are openly available for the popular PyTorch library, and existing implementations are often limited to two-dimensional data and classification models. We present M3d-CAM, an easy easy to use library for generating attention maps of CNN-based PyTorch models for both 2D and 3D data, and applicable to both classification and segmentation models. The attention maps can be generated with multiple methods: Guided Backpropagation, Grad-CAM, Guided Grad-CAM and Grad-CAM++. The maps visualize the regions in the input data that most heavily influence the model prediction at a certain layer. Only a single line of code is sufficient for generating attention maps for a model, making M3d-CAM a plug-and-play solution that requires minimal previous knowledge.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Gotkowski, Karol ; Gonzalez, Camila ; Bucher, Andreas ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | M3d-CAM: a PyTorch Library to Generate 3D Attention Maps for Medical Deep Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 27 Februar 2021 |
| Verlag: | Springer |
| Buchtitel: | Bildverarbeitung für die Medizin 2021 |
| Reihe: | Informatik aktuell |
| Veranstaltungstitel: | German Workshop on Medical Image Computing |
| Veranstaltungsort: | Regensburg, Germany |
| Veranstaltungsdatum: | 07.03.2021-09.03.2021 |
| DOI: | 10.1007/978-3-658-33198-6_52 |
| Kurzbeschreibung (Abstract): | Deep learning models achieve state-of-the-art results in a wide array of medical imaging problems. Yet the lack of interpretability of deep neural networks is a primary concern for medical practitioners and poses a considerable barrier before the deployment of such models in clinical practice. Several techniques have been developed for visualizing the decision process of DNNs. However, few implementations are openly available for the popular PyTorch library, and existing implementations are often limited to two-dimensional data and classification models. We present M3d-CAM, an easy easy to use library for generating attention maps of CNN-based PyTorch models for both 2D and 3D data, and applicable to both classification and segmentation models. The attention maps can be generated with multiple methods: Guided Backpropagation, Grad-CAM, Guided Grad-CAM and Grad-CAM++. The maps visualize the regions in the input data that most heavily influence the model prediction at a certain layer. Only a single line of code is sufficient for generating attention maps for a model, making M3d-CAM a plug-and-play solution that requires minimal previous knowledge. |
| Zusätzliche Informationen: | Aktuelle Forschungsergebnisse Deckt alle Bereiche der medizinischen Bildverarbeitung ab Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Feb 2022 09:25 |
| Letzte Änderung: | 16 Feb 2022 09:25 |
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