Abstract
In this study, we present a method to identify radiologic findings associated with interstitial lung diseases (ILD), a heterogeneous collection of progressive lung diseases, from thoracic CT scans. Prior studies have relied on densely supervised methods using 2D slices or small 3D patches as input, requiring significant manual labor to create dense labels. This limits the amount of data available for algorithm development and thus hinders generalization performance. To harness available large, but sparsely labeled datasets, we present a weakly supervised method to identify imaging findings associated with ILD. We test this framework to classify and roughly localize 14 radiologic findings on the LTRC dataset of 3380 thoracic CT scans. We conduct 5-fold cross-validation and achieve 0.8 mean AUC scores on 5 out of 14 findings classification. We visualize attention energy maps which demonstrate that our classifier is able to learn representative features with meaningful differences between radiologic findings, and is capable of approximately localizing the findings of interest, thereby adding interpretability of our model (This work was supported by the USPHS under NIH grant R01-HL133889).
Y. Wu and J. Wang—Equal contribution.
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Wu, Y., Wang, J., Lindsay, W.D., Wen, T., Shi, J., Gee, J.C. (2020). Interpretable Identification of Interstitial Lung Disease (ILD) Associated Findings from CT. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_54
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DOI: https://doi.org/10.1007/978-3-030-59725-2_54
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