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, how- ever, 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 physi- cian during the entire peri-diagnostic workflow, including the acquisition stage. We model the following question, asked from a physician’s per- spective: “Given the evidence collected so far, which examination should I perform next, in order to achieve the most accurate and efficient diag- nostic prediction?”. In this work, we propose a novel approach which is enticingly simple: use dropout at the input layer, and integrated gradi- ents 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.
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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, how- ever, 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 physi- cian du...
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