Abstract
Malignant bone lesions often lead to poor prognosis if not detected and treated in time. It also influences the treatment plan for primary tumor. However, diagnosing these lesions can be challenging due to their subtle appearance resemblances to other pathological conditions. Precise segmentation can help identify lesion types but the regions of interest (ROIs) are often difficult to delineate, particularly for bone lesions. We propose a bone lesion identification network (BLIN) in whole body non-contrast CT scans based on weakly supervised learning through class activation map (CAM). In the algorithm, location of the focal box of each lesion is used to supervise network training through CAM. Compared with precise segmentation, focal boxes are relatively easy to be obtained either by manual annotation or automatic detection algorithms. Additionally, to deal with uneven distribution of training samples of different lesion types, a new sampling strategy is employed to reduce overfitting of the majority classes. Instead of using complicated network structures such as grouping and ensemble for long-tailed data classification, we use a single-branch structure with CBAM attention to prove the effectiveness of the weakly supervised method. Experiments were carried out using bone lesion dataset, and the results showed that the proposed method outperformed the state-of-the-art algorithms for bone lesion classification.
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Deng, K., Wang, B., Ma, S., Xue, Z., Cao, X. (2024). A Bone Lesion Identification Network (BLIN) in CT Images with Weakly Supervised Learning. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_25
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