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The study investigates whether pathology foundation models can enable training‑free, content‑based retrieval of similar cases from digitized archives of salivary gland tumors. Using a region of interest from a whole slide image, the models retrieve morphologically similar cases and their ICD‑O codes without task‑specific training. Evaluation on 137 cases across 12 morphology categories shows that Atlas2 achieves the highest retrieval accuracy, with macro‑averaged F1 scores ranging from 69.0% to 76.6% across models.
arXiv:2610.14226v1 Announce Type: cross Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation...
WILSON is a vision–language foundation model that represents whole‑slide images and multi‑slide patient cases as single multi‑magnification composite images. Trained on about 189,000 Mayo Clinic slides covering 42 organs and 829 diagnostic entities, it outperforms dedicated case‑level models on internal cohorts and matches slide‑level models while using far less compute. Fine‑tuning on triple‑negative breast cancer data improves histologic subtyping and lymphocyte grading, and the model retrieves diagnostic text with high recall and generates captions closer to report references than prior methods.
arXiv:2605. 24253v2 Announce Type: replace-cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity.
arXiv:2510.23224v2 Announce Type: replace Abstract: The rapid digitization of histopathology slides has opened new opportunities for computational tools in clinical and research workflows. Content-ba...
arXiv:2609.32876v2 Announce Type: replace-cross Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons...