In this work, two deep learning models, trained to segment the liver and perform depth reconstruction, are compared and analysed with their post-hoc explanation interplay. The first model (a U-Net) is designed to perform liver semantic segmentation over different subjects and scenarios. Particularly, the image pixels representing the liver are classified and separated by the surrounding pixels. Meanwhile, with the second model, a depth estimation is performed to regress the z-position of each pixel (relative depths). In general, these two models apply a sort of classification task which can be explained for each model individually and that can be combined to show additional relations and insights between the most relevant learned features. In detail, this work shows how post-hoc explainable AI systems (X-AI) based on Grad CAM and Grad CAM++ can be compared by introducing Cross X-AI (CX-AI). Typically the post-hoc explanation maps provide different visual explanations of their decisions based on the two proposed approaches. Our results show that the Grad Cam++ segmentation explanation maps present cross-learning strategies similar to disparity explanations (and vice versa).

Cross X-AI: Explainable Semantic Segmentation of Laparoscopic Images in Relation to Depth Estimation

Bardozzo F.;Delli Priscoli M.;Tagliaferri R.
2022-01-01

Abstract

In this work, two deep learning models, trained to segment the liver and perform depth reconstruction, are compared and analysed with their post-hoc explanation interplay. The first model (a U-Net) is designed to perform liver semantic segmentation over different subjects and scenarios. Particularly, the image pixels representing the liver are classified and separated by the surrounding pixels. Meanwhile, with the second model, a depth estimation is performed to regress the z-position of each pixel (relative depths). In general, these two models apply a sort of classification task which can be explained for each model individually and that can be combined to show additional relations and insights between the most relevant learned features. In detail, this work shows how post-hoc explainable AI systems (X-AI) based on Grad CAM and Grad CAM++ can be compared by introducing Cross X-AI (CX-AI). Typically the post-hoc explanation maps provide different visual explanations of their decisions based on the two proposed approaches. Our results show that the Grad Cam++ segmentation explanation maps present cross-learning strategies similar to disparity explanations (and vice versa).
978-1-7281-8671-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4807756
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