Abstract
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods. The source code will be publicly available upon publication.
Keywords
- Visual explanations
- Covid-19
- Weakly supervised
- Information bottleneck attribution
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References
Bagci, U., et al.: A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging. EJNMMI Res. 3(1), 55 (2013)
Chassagnon, G., et al.: Ai-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Med. Image Anal. 67, 101860 (2021)
Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839–847 (2018). https://doi.org/10.1109/WACV.2018.00097
Dubost, F., et al.: Weakly supervised object detection with 2D and 3D regression neural networks. Med. Image Anal. 65, 101767 (2020)
Eitel, F., Ritter, K.: Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification. In: Suzuki, K., et al. (eds.) ML-CDS/IMIMIC -2019. LNCS, vol. 11797, pp. 3–11. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33850-3_1
Harmon, S.A., et al.: Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 11(1), 4080 (2020)
Li, K., et al.: CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur. Radiol. 30(8), 4407–4416 (2020)
Li, K., Wu, Z., Peng, K.C., Ernst, J., Fu, Y.: Tell me where to look: guided attention inference network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Li, Z., et al.: A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning. Med. Image Anal. 69, 101978 (2021)
Morozov, S.P., et al.: MosMedData: chest CT scans with COVID-19 related findings dataset (2020)
Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Bhardwaj, P., Singh, V.: A deep learning and grad-cam based color visualization approach for fast detection of COVID-19 cases using chest x-ray and CT-scan images. Chaos, Solitons Fractals 140, 110190 (2020)
Schulz, K., Sixt, L., Tombari, F., Landgraf, T.: Restricting the flow: information bottlenecks for attribution. In: International Conference on Learning Representations (2020). https://openreview.net/forum?id=S1xWh1rYwB
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2017
Shan, F., et al.: Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med. Phys. 48(4), 1633–1645 (2021). https://doi.org/10.1002/mp.14609
Tang, Z., et al.: Severity assessment of COVID-19 using CT image features and laboratory indices. Phys. Med. Biol. 66(3), 035015 (2021)
Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method, pp. 368–377 (1999)
Young, K., Booth, G., Simpson, B., Dutton, R., Shrapnel, S.: Deep neural network or dermatologist? In: Suzuki, K., et al. (eds.) ML-CDS/IMIMIC -2019. LNCS, vol. 11797, pp. 48–55. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33850-3_6
Zhang, H., et al.: ResNeSt: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)
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Demir, U. et al. (2021). Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_41
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DOI: https://doi.org/10.1007/978-3-030-87589-3_41
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