Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billions of parameters on scarce, expert-annotated pathology data is prohibitive, while in-context learning (ICL), which conditions the VLM on demonstrative image-text pairs without parameter updates, suffers from high sensitivity to which examples are selected and how the query is phrased, producing unreliable diagnostics. Existing selection strategies rely on query-dependent nearest-neighbour retrieval that ignores global data structure, require costly parameter updates, or disregard the joint vision-text embedding g...
| Authors | Franciskus Xaverius Erick, Johanna Paula Müller, Bernhard Kainz |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 7 July 2026 |
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