Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Vivar G, Mullakaeva K, Zwergal A, Navab N, Ahmadi SA (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12262 LNCS
Pages Range: 572-581
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030597122
DOI: 10.1007/978-3-030-59713-9_55
Abstract
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high-dimensional multimodal examination data, and often assume feature-completeness. This, however, is rarely the case due to examination costs, invasiveness, or a lack of indication. A sub-problem in CADx, which to our knowledge has not been addressed by the MICCAI community so far, is to guide the physician during the entire peri-diagnostic workflow, including the acquisition stage. We model the following question, asked from a physician’s perspective: “Given the evidence collected so far, which examination should I perform next, in order to achieve the most accurate and efficient diagnostic prediction?”. In this work, we propose a novel approach which is enticingly simple: use dropout at the input layer, and integrated gradients of the trained network at test-time to attribute feature importance dynamically. We validate and explain the effectiveness of our proposed approach using two public medical and two synthetic datasets. Results show that our proposed approach is more cost- and feature-efficient than prior approaches and achieves a higher overall accuracy. This directly translates to less unnecessary examinations for patients, and a quicker, less costly and more accurate decision support for the physician.
Involved external institutions
Germany (DE)How to cite
APA:
Vivar, G., Mullakaeva, K., Zwergal, A., Navab, N., & Ahmadi, S.A. (2020). Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 572-581). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Vivar, Gerome, et al. "Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 572-581.
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