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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.05034 |
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| _version_ | 1866915604120731648 |
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| author | Jin, Jing Liu, Xu Gao, Te Shi, Zhihong Liang, Yixiong Zheng, Ruiqing Kuang, Hulin Zeng, Min Kan, Shichao |
| author_facet | Jin, Jing Liu, Xu Gao, Te Shi, Zhihong Liang, Yixiong Zheng, Ruiqing Kuang, Hulin Zeng, Min Kan, Shichao |
| contents | Whole Slide Image (WSI) representation is critical for cancer subtyping, cancer recognition and mutation prediction.Training an end-to-end WSI representation model poses significant challenges, as a standard gigapixel slide can contain tens of thousands of image tiles, making it difficult to compute gradients of all tiles in a single mini-batch due to current GPU limitations. To address this challenge, we propose a method of dynamic residual encoding with slide-level contrastive learning (DRE-SLCL) for end-to-end WSI representation. Our approach utilizes a memory bank to store the features of tiles across all WSIs in the dataset. During training, a mini-batch usually contains multiple WSIs. For each WSI in the batch, a subset of tiles is randomly sampled and their features are computed using a tile encoder. Then, additional tile features from the same WSI are selected from the memory bank. The representation of each individual WSI is generated using a residual encoding technique that incorporates both the sampled features and those retrieved from the memory bank. Finally, the slide-level contrastive loss is computed based on the representations and histopathology reports ofthe WSIs within the mini-batch. Experiments conducted over cancer subtyping, cancer recognition, and mutation prediction tasks proved the effectiveness of the proposed DRE-SLCL method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation Jin, Jing Liu, Xu Gao, Te Shi, Zhihong Liang, Yixiong Zheng, Ruiqing Kuang, Hulin Zeng, Min Kan, Shichao Computer Vision and Pattern Recognition Artificial Intelligence I.4.9; I.2.10 Whole Slide Image (WSI) representation is critical for cancer subtyping, cancer recognition and mutation prediction.Training an end-to-end WSI representation model poses significant challenges, as a standard gigapixel slide can contain tens of thousands of image tiles, making it difficult to compute gradients of all tiles in a single mini-batch due to current GPU limitations. To address this challenge, we propose a method of dynamic residual encoding with slide-level contrastive learning (DRE-SLCL) for end-to-end WSI representation. Our approach utilizes a memory bank to store the features of tiles across all WSIs in the dataset. During training, a mini-batch usually contains multiple WSIs. For each WSI in the batch, a subset of tiles is randomly sampled and their features are computed using a tile encoder. Then, additional tile features from the same WSI are selected from the memory bank. The representation of each individual WSI is generated using a residual encoding technique that incorporates both the sampled features and those retrieved from the memory bank. Finally, the slide-level contrastive loss is computed based on the representations and histopathology reports ofthe WSIs within the mini-batch. Experiments conducted over cancer subtyping, cancer recognition, and mutation prediction tasks proved the effectiveness of the proposed DRE-SLCL method. |
| title | Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.4.9; I.2.10 |
| url | https://arxiv.org/abs/2511.05034 |