Abstract
Accurate nodule labeling and interpretable machine learning are important for lung cancer diagnosis. To circumvent the label ambiguity issue of commonly-used unsure nodule data such as LIDC-IDRI, we constructed a sure nodule data with gold-standard clinical diagnosis. To make the traditional CNN networks interpretable, we propose herewith a novel collaborative model to improve the trustworthiness of lung cancer predictions by self-regulation, which endows the model with the ability to provide explanations in meaningful terms to a human-observer. The proposed collaborative model transfers domain knowledge from unsure data to sure data and encodes a cause-and-effect logic based on nodule segmentation and attributes. Further, we construct a regularization strategy that treats the visual saliency maps (Grad-CAM) not only as post-hoc model interpretation, but also as a rational measure for trustworthy learning in such a way that the CNN features are extracted mainly from intrinsic nodule features. Moreover, similar nodule retrieval makes a nodule diagnosis system more understandable and credible to humans-observers based on the nodule attributes. We demonstrate that the combination of the collaborative model and regularization strategy can provide the best performances on lung cancer prediction and interpretable diagnosis that can automatically: 1) classify the nodule patches; 2) analyse and explain a prediction by nodule segmentation and attributes; and 3) retrieve similar nodules for comparison and diagnosis.
H. Zhang and L. Chen—Joint first authors of this work.
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Acknowledgment
This work was partly supported by Medicine-Engineering Interdisciplinary Research Foundation of Shanghai Jiao Tong University (YG2021QN128), Shanghai Sailing Program (20YF1420800), National Nature Science Foundation of China (No.62003208), Shanghai Municipal of Science and Technology Project (Grant No. 20JC1419500), and Science and Technology Commission of Shanghai Municipality (Grant 20DZ2220400).
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Zhang, H. et al. (2022). Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging. In: Reyes, M., Henriques Abreu, P., Cardoso, J. (eds) Interpretability of Machine Intelligence in Medical Image Computing. iMIMIC 2022. Lecture Notes in Computer Science, vol 13611. Springer, Cham. https://doi.org/10.1007/978-3-031-17976-1_1
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