Evaluation of Randomized Input Sampling for Explanation (RISE) for 3D XAI - Proof of Concept for Black-Box Brain-Hemorrhage Classification

Jack Highton, Quok Zong Chong, Richard Crawley, Julia A. Schnabel, Kanwal K. Bhatia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An increasing number of AI products for medical imaging solutions are offered to healthcare organizations, but frequently these are considered to be a ‘black-box’, offering only limited insights into the AI model functionality. Therefore, model-agnostic methods are required to provide Explainable AI (XAI) in order to improve clinicians’ trust and thus accelerate adoption. However, there is a current lack of published methods to explain 3D classification models with systematic evaluation for medical imaging applications. Here, the popular explainability method RISE is modified so that, for the first time to the best of our knowledge, it can be applied to 3D medical image classification. The method was assessed using recently proposed guidelines for clinical explainable AI. When different parameters were tested using a 3D CT dataset and a classifier to detect the presence of brain hemorrhage, we found that combining different algorithms to produce 3D occlusion patterns led to better and more reliable explainability results. This was confirmed using both quantitative metrics and interpretability assessment of the 3D saliency heatmaps by a clinical expert.

Original languageEnglish
Title of host publicationProceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) - Medical Imaging and Computer-Aided Diagnosis
EditorsRuidan Su, Yu-Dong Zhang, Alejandro F. Frangi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages41-51
Number of pages11
ISBN (Print)9789819713349
DOIs
StatePublished - 2024
EventInternational Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2023 - Cambridge, United Kingdom
Duration: 9 Dec 202310 Dec 2023

Publication series

NameLecture Notes in Electrical Engineering
Volume1166 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2023
Country/TerritoryUnited Kingdom
CityCambridge
Period9/12/2310/12/23

Keywords

  • Explainable AI
  • Hemorrhage Classification
  • RISE

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