Lubin Gan, Jing Zhang, Mingjun Ma, Linhao Qu, Dachun Kai, Xiaoyan Sun · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29746 · open-access PDF
全幻灯片(WSI)分析允许计算诊断和预测,但Gigapixel图像和有限的注释使幻灯片级的学习具有挑战性。多实例学习通过从冻结的预训练编码器中汇总补丁表示来解决这个任务。最近,Mamba 允许有效地建模长的补丁序列。然而,单独的任务监督并不明确地限制功能调整期间的信息恢复,而Mamba 则将本地处理与长距离扩散相结合,而不是保留单独的代表来进行合并。
Machine translation (Meta M2M100); the English original follows.
Whole slide image (WSI) analysis enables computational diagnosis and prognosis, but gigapixel images and limited annotations make slide-level learning challenging. Multiple instance learning addresses this task by aggregating patch representations from frozen pretrained encoders. Recently, Mamba has enabled efficient modeling of long patch sequences. However, task supervision alone does not explicitly constrain information recovery during feature adaptation, while Mamba couples local processing with long-range propagation rather than retaining separate representations for fusion. Motivated by
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