2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-2.8
Paper Title INTERPRETATION OF LESIONAL DETECTION VIA COUNTERFACTUAL GENERATION
Authors Junho Kim, Minsu Kim, Yong Man Ro, Korea Advanced Institute of Science and Technology, Republic of Korea
SessionBIO-2: Biomedical Signal Processing 2
LocationArea D
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract To interpret the decision of Deep Neural Networks (DNNs), explainable artificial intelligence research has been widely investigated. Especially, visualizing the attribution maps is known as one of the efficient ways to provide explanations for the trained networks. However, applying existing visualization methods on medical images has significant issues in that the medical images commonly have inherent imbalanced data poses and scarcity. To tackle such issues and provide more accurate explanations in medical images, in this paper, we propose a new explainable framework, Counterfactual Generative Network (CGN). We embed counterfactual lesion prediction of DNNs to our explainable framework as prior conditions and guide to generate various counterfactual lesional images from normal input sources, or vice versa. By doing so, CGN can represent detailed attribution maps and generate corresponding normal images from leisonal inputs. Extensive experiments are conducted on the two chest X-ray datasets to verify the effectiveness of our method.