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Abstract #2400

Interpretability Techniques for Deep Learning based Segmentation Models

Soumick Chatterjee1,2,3, Arnab Das3, Chirag Mandal3, Budhaditya Mukhopadhyay3, Manish Vipinraj3, Aniruddh Shukla3, Oliver Speck1,4,5,6, and Andreas Nürnberger2,3,6
1Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 4German Centre for Neurodegenerative Diseases, Magdeburg, Germany, 5Leibniz Institute for Neurobiology, Magdeburg, Germany, 6Center for Behavioral Brain Sciences, Magdeburg, Germany

In medical image analysis, it is desirable to decipher the black-box nature of Deep Learning models in order to build confidence in clinicians while using such methods. Interpretability techniques can help understand the model’s reasonings, e.g. by showcasing the anatomical areas the network focuses on. While most of the available interpretability techniques work with classification models, this work presents various interpretability techniques for segmentation models and shows experiments on a vessel segmentation model. In particular, we focus on input attributions and layer attribution methods which give insights on the critical features of the image identified by the model.

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