Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer, and a slide-level aggregator. While encoders and aggregators have been extensively studied, the projection layer remains a largely morphology-only bottleneck. This limits endpoints such as biomarker status and survival, which are governed by a molecular state that is not fully captured by H&E morphology. We introduce Molecularly Informed Staining Transform (MIST), a plug-in replacement for the MIL projection layer that uses paired spatial transcriptomics only during training to construct virtual molecular stai...
| Authors | Yucheng Xing, Pei Liu, Jingying Ma et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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