Abstract
Radiologists identify, measure, and classify clinically significant lesions routinely for cancer staging and tumor burden assessment. As these tasks are repetitive and cumbersome, only the largest lesion is identified leaving others of potential importance unmentioned. Automated deep learning-based methods for lesion detection have been proposed in literature to help relieve their tasks with the publicly available DeepLesion dataset (32,735 lesions, 32,120 CT slices, 10,594 studies, 4,427 patients, 8 body part labels). However, this dataset contains missing lesions, and displays a severe class imbalance in the labels. In our work, we use a subset of the DeepLesion dataset (boxes + tags) to train a state-of-the-art VFNet model to detect and classify suspicious lesions in CT volumes. Next, we predict on a larger data subset (containing only bounding boxes) and identify new lesion candidates for a weakly-supervised self-training scheme. The self-training is done across multiple rounds to improve the model’s robustness against noise. Two experiments were conducted with static and variable thresholds during self-training, and we show that sensitivity improves from 72.5% without self-training to 76.4% with self-training. We also provide a structured reporting guideline through a “Lesions” sub-section for entry into the “Findings” section of a radiology report. To our knowledge, we are the first to propose a weakly-supervised self-training approach for joint lesion detection and tagging in order to mine for under-represented lesion classes in the DeepLesion dataset.
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This work was supported by the Intramural Research Program of the National Institutes of Health (NIH) Clinical Center.
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Naga, V., Mathai, T.S., Paul, A., Summers, R.M. (2022). Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_6
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