ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual ScansDownload PDF

Published: 11 May 2021, Last Modified: 29 Apr 2024MIDL 2021 PosterReaders: Everyone
Keywords: Interpretable, Classification, Regression, Deep Generative Networks
TL;DR: This paper presents a framework for regression with feature attribution using deep generative methods.
Abstract: Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification and regression problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present an extension of the ICAM framework for creating prediction specific FA maps through image-to-image translation.
Paper Type: both
Primary Subject Area: Interpretability and Explainable AI
Secondary Subject Area: Application: Radiology
Paper Status: based on accepted/submitted journal paper
Source Code Url: https://github.com/CherBass/ICAM
Data Set Url: https://www.ukbiobank.ac.uk/
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Source Latex: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2103.02561/code)
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