arXiv AI
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 8
Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context
arXiv:2607. 05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone.
arXiv AI
Sep 25
Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
arXiv AI
Aug 11
Distilling CT Foundation Models into Editable Concept Bottlenecks for Lung Nodule Malignancy Prediction
arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.
arXiv Machine Learning
Sep 23
WILSON - a pathology foundation model framework for patient-level analysis and diagnostic text generation
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 Computer Vision
Sep 30
HERO: Histology Encoder for Robust Representation in Oncology
HERO (Histology Encoder for Robust Representation in Oncology) is a ViT‑G/14 pathology foundation model trained with DINO and iBOT objectives and refined using high‑resolution Gram anchoring on a 500‑million‑tile corpus from about 575,000 clinical whole‑slide images. It demonstrates superior robustness to center, scanner, and stain variation compared to other state‑of‑the‑art foundation models, while maintaining competitive performance on tile‑level classification, segmentation, and gene‑expression prediction. Across 39 slide‑level clinical tasks, HERO ranks first on average and achieves the best average rank across six benchmark frameworks under an equal‑weighted analysis.
arXiv Computer Vision
3d ago
Pathology foundation models for training-free case retrieval in salivary gland tumors
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.