Abstract
In this paper, we address the segmentation of tumor subtypes in whole slide images (WSI) by utilizing incomplete label proportions. Specifically, we utilize ‘partial’ label proportions, which give the proportions among tumor subtypes but do not give the proportion between tumor and non-tumor. Partial label proportions are recorded as the standard diagnostic information by pathologists, and we, therefore, want to use them to realize the segmentation model that can classify each WSI patch into one of the tumor subtypes or non-tumor. We call this problem “learning from partial label proportions (LPLP)” and formulate the problem as a weakly supervised learning problem. Then, we propose an efficient algorithm for this challenging problem by decomposing it into two weakly supervised learning subproblems: multiple instance learning (MIL) and learning from label proportions (LLP). These subproblems are optimized efficiently in an end-to-end manner. The effectiveness of our algorithm is demonstrated through experiments conducted on two WSI datasets. This code is available at https://github.com/matsuo-shinnosuke/LPLP.
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Acknowledgement
This work was supported by JSPS-JP23KJ1723, JST-JPMJAX23CR, JSPS-JP21K18312, JSPS-JP23K18509, and SIP-JPJ01242.
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Matsuo, S. et al. (2024). Learning from Partial Label Proportions for Whole Slide Image Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15011. Springer, Cham. https://doi.org/10.1007/978-3-031-72120-5_35
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DOI: https://doi.org/10.1007/978-3-031-72120-5_35
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