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Sadafi, A. ; Adonkina, O. ; Khakzar, A.* ; Lienemann, P. ; Hehr, M. ; Rueckert, D.* ; Navab, N.* ; Marr, C.

Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images.

In: (Information Processing in Medical Imaging). Berlin [u.a.]: Springer, 2023. 170-182 (Lect. Notes Comput. Sc. ; 13939 LNCS)
DOI
Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the task of diagnosing blood cancer from patients’ blood smears. We study two datasets of acute myeloid leukemia with over 100 000 single cell images and observe how each attribution method performs on the multiple instance learning architecture focusing on different properties of the white blood single cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model’s decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights for clinicians to better understand and trust decisions from computer-aided diagnosis systems.
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Publication type Article: Conference contribution
Keywords Blood Cancer Cytology ; Multiple Instance Learning ; Pixel-level Explainability; Classification
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Information Processing in Medical Imaging
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 13939 LNCS, Issue: , Pages: 170-182 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540007-001
DOI 10.1007/978-3-031-34048-2_14
WOS ID 001116102900014
Scopus ID 85163954275
Erfassungsdatum 2023-10-18
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