Multiple Instance Learning
Cross-source consensus on Multiple Instance Learning from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Comparisons
Background
Highlighted claims
- Multiple instance learning has become the standard framework for whole-slide image analysis in computational pathology. — Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
- The canonical MIL pipeline decomposes a gigapixel slide into patches, encodes them with a frozen foundation model, applies a per-patch projection layer, and aggregates into a slide-level prediction. — Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
- MIST is evaluated as a plug-in projection-layer replacement across eight distinct MIL aggregators. — Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
- Prior MIL research has invested heavily in the encoder and aggregation stages, leaving the projection layer comparatively underexplored. — Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining