Category feature reconstruction for pathological image segmentation

Zhibang Zhou; Dehui Xiang; Fei Shi; Weifang Zhu; Xinjian Chen

doi:10.1117/12.2651394

6 April 2023 Category feature reconstruction for pathological image segmentation

Zhibang Zhou, Dehui Xiang, Fei Shi, Weifang Zhu, Xinjian Chen

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711X (2023) https://doi.org/10.1117/12.2651394
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

In recent works, more and more attention mechanisms have been used for medical image segmentation, however, attention mechanisms are not very good at distinguishing categories in multi-category medical image segmentation tasks. In this paper, we propose a category feature reconstruction module (CFRM) for multi-category pathological image segmentation of pancreatic adenosquamous carcinoma. Compared with the attention mechanism to enhance the features of the region of interest, the proposed CFRM pays more attention to the reconstruction of category features. The CFRM enables the network not only to highlight the features of the region of interest, but also to increase the discrimination of different categories of features. Based on UNet, the proposed CFRM is added at the top of the encoding path. Compared with other state-of-art methods, both the Dice coefficients and the Iou coefficients of the proposed method have reached the best level on our pancreatic adenosquamous carcinoma segmentation dataset.

Citation Download Citation

Zhibang Zhou, Dehui Xiang, Fei Shi, Weifang Zhu, and Xinjian Chen "Category feature reconstruction for pathological image segmentation", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711X (6 April 2023); https://doi.org/10.1117/12.2651394

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