Abstract
Deep learning models may be useful for the differential diagnosis of breast cancer histopathology images. However, most modern deep learning methods are data-hungry. But, large annotated dataset of breast cancer histopathology images are elusive. As a result, the application of such deep learning methods for the differential diagnosis of breast cancer is limited. To deal with this problem, we propose a few-shot learning approach for the differential diagnosis of the histopathology images of breast tissue. Our model is trained through two stages. We initially train our model for a binary classification task of identifying benign and malignant tissues. Subsequently, we propose a multi-task learning strategy for the few-shot differential diagnosis of breast tissues. Experiments on publicly available breast cancer histopathology image datasets show the efficacy of the proposed method.
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Thoriya, K., Mutreja, P., Kalra, S., Paul, A. (2023). Multi-task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_19
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DOI: https://doi.org/10.1007/978-3-031-44917-8_19
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