Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
Khakzar A, Musatian S, Buchberger J, Quiroz IV, Pinger N, Baselizadeh S, Kim ST, Navab N (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12903 LNCS
Pages Range: 499-508
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030871987
DOI: 10.1007/978-3-030-87199-4_47
Abstract
Convolutional neural networks are showing promise in the automatic diagnosis of thoracic pathologies on chest x-rays. Their black-box nature has sparked many recent works to explain the prediction via input feature attribution methods (aka saliency methods). However, input feature attribution methods merely identify the importance of input regions for the prediction and lack semantic interpretation of model behavior. In this work, we first identify the semantics associated with internal units (feature maps) of the network. We proceed to investigate the following questions; Does a regression model that is only trained with COVID-19 severity scores implicitly learn visual patterns associated with thoracic pathologies? Does a network that is trained on weakly labeled data (e.g. healthy, unhealthy) implicitly learn pathologies? Moreover, we investigate the effect of pretraining and data imbalance on the interpretability of learned features. In addition to the analysis, we propose semantic attribution to semantically explain each prediction. We present our findings using publicly available chest pathologies (CheXpert [5], NIH ChestX-ray8 [25]) and COVID-19 datasets (BrixIA [20], and COVID-19 chest X-ray segmentation dataset [4]). The Code (https://github.com/CAMP-eXplain-AI/CheXplain-Dissection ) is publicly available.
Involved external institutions
Germany (DE)
Korea, Republic of (KR)How to cite
APA:
Khakzar, A., Musatian, S., Buchberger, J., Quiroz, I.V., Pinger, N., Baselizadeh, S.,... Navab, N. (2021). Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models. In Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 499-508). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Khakzar, Ashkan, et al. "Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 499-508.
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