Newest papers

LEAF-Mamba: Feature Adaptation and Fusion for Whole Slide Image Analysis

Lubin Gan, Jing Zhang, Mingjun Ma, Linhao Qu, Dachun Kai, Xiaoyan Sun · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29746 · open-access PDF

Japanese

Whole slide image (WSI) analysis allows 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 pre-trained 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 pairs local processing with long-range propagation rather than retaining separate representations for fusion. 機能の調整中に情報回復を明示的に制限するものではありません。

Machine translation (Meta M2M100); the English original follows.

Abstract

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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