Newest papers

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

Lizhao Gan, Jing Zhang, Mingjun Ma, Linhao Qu, Dachun Kai, Xiaoyan Sun · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29746 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

El análisis de imágenes de diapositivas completas (WSI) permite el diagnóstico y el pronóstico computacional, pero las imágenes de gigapíxeles y las anotaciones limitadas hacen que el aprendizaje efectivo a nivel de diapositivas sea desafiante. El aprendizaje de instancias múltiples aborda esta tarea agregando representaciones de parches de codificadores pre-codificados congelados. Recientemente, Mamba ha permitido una modelización eficiente de secuencias de parches largos. Sin embargo, la supervisión de tareas por sí sola no limita explícitamente la recuperación de la información durante la adaptación de funciones, mientras que Mamba combina el procesamiento local con propagación a largo plazo en lugar de retener representaciones separadas para la fusión. Motivados por estos retos, introducimos LEAF-Mamba, un marco que ab

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 these challenges, we introduce LEAF-Mamba, a framework that jointly addresses latent evidence adaptation and fusion for frozen-feature WSI analysis. A reconstruction-regularized adapter learns compact, task-specific representations while reconstructing the input features. Parallel Mamba and convolutional streams then capture long-range dependencies and sequence-local interactions, with an input-dependent gate modulating their joint representation before slide-level pooling. Experiments across feature extractors demonstrate improved average performance over strong MIL baselines in both diagnostic classification and survival prediction. Component analyses further support the contributions of adaptation and fusion. These findings highlight the value of jointly designing feature transformation and contextual aggregation to translate pretrained representations into effective slide-level predictions.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Artificial intelligence, Feature (linguistics), Task (project management), Representation (politics), Feature learning

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange