The paper introduces GloSAGE, a semantic self‑distillation framework that leverages lesion profiles from a pretrained visual teacher to generate structured pathology descriptions for glomerular images. By selecting reliable image‑text anchors and synthesizing knowledge‑structured text, GloSAGE trains a frozen visual encoder with an intermediate semantic transfer layer, enabling zero‑shot lesion recognition, image‑text retrieval, and improved downstream tasks such as disease classification and renal function estimation. Experiments on the GloMillion dataset and seven cohorts demonstrate that GloSAGE outperforms its source visual model in most lesion‑recognition tasks and enhances grading accuracy while maintaining competitive performance on case‑level analyses.
arXiv:2607. 11257v1 Announce Type: cross Abstract: Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs.
arXiv:2609.00866v1 Announce Type: cross Abstract: The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pa...
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remains limited by the sc...
PathLang is a new, language‑centered benchmark for vision‑language models in computational pathology. It keeps the underlying pathology slides, ground‑truth labels, and image‑text evaluation direction constant while systematically varying the diagnostic language used in prompts, reflecting how pathologists actually rephrase diagnoses. The benchmark covers four task families—zero‑shot classification with image‑text alignment, cross‑modal retrieval, paraphrase robustness, and open‑vocabulary diagnosis retrieval—and evaluates nine VLMs across five public datasets spanning four organs.
The paper introduces FFM-CP, a framework that fuses multiple pathology vision‑language foundation models for few‑shot learning. It aligns heterogeneous representations with an Orthogonal Procrustes transformation, then uses a unified graph to refine support‑image features and class prototypes across backbones. Experiments on six histopathology datasets show that FFM‑CP outperforms the best single adapted model in 50 of 54 few‑shot comparisons.
arXiv:2608. 04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis.
arXiv:2607.19261v4 Announce Type: replace-cross Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating...
The paper introduces Gated Knowledge‑Guided Prototype Transfer (GProtoT), a training‑free module that enhances zero‑support medical vision‑language models by transferring visual residuals from supported classes to missing ones using VLM similarity and clinical ontology distances. GProtoT employs an evidence gate to scale transfer based on semantic relevance, reducing negative transfer while modestly improving recall and accuracy in realistic one‑shot trials on MESSIDOR. The method requires no gradients, validation data, or extra inference, offering a low‑compute, accountable adaptation path for incomplete clinical supervision.
SCOUT is a concept‑grounded multimodal transformer that generates whole‑slide pathology reports by integrating local histological patterns, whole‑slide context, and expert‑curated diagnostic concepts. It uses evolving visual representations and recursively updated slide‑ and concept‑conditioned representations, with separate attention pathways during decoding that are fused adaptively for each token. Evaluated on TCGA‑BRCA, HistAI, and REG‑2025, SCOUT outperformed existing methods, improving BLEU, METEOR, and ROUGE‑L scores and raising the Clinical Report Quality Score on REG‑2025.
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:2608. 15580v1 Announce Type: new Abstract: Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morphological description within a single record.
The paper introduces Expandable Modality Experts, a medical vision‑language framework that uses an expandable bank of modality experts and a Multiway Transformer to combine shared visual computation with deterministic routing and a separate text pathway. It employs a modality‑aware visual tokenizer for masked pretraining on 19.4 million unpaired image rows across seven modalities, followed by contrastive alignment on 3.23 million image‑text pairs. With a ViT‑Large backbone, the method achieves state‑of‑the‑art results in 24 of 25 benchmark settings across nine modalities, including zero‑shot classification, linear probing, and retrieval tasks, while preserving existing representations.