An end-to-end-trained vision-language model generates prostate biopsy reports in native languages, demonstrated in German. The system uses a tokenizer and model trained from scratch and an automated pipeline that splits composite reports into image-text pairs, producing 17,344 pairs from 2,402 cases without manual annotation. Evaluated on clinical attributes, it achieves 96.2% F1 for malignancy detection and 65.2% for Gleason grading, comparable to an FDA-cleared classifier and validated on external cohorts.
arXiv:2511. 20956v2 Announce Type: replace-cross Abstract: Breast ultrasound (BUS) reporting relies on clinically meaningful lesion descriptors, including BI-RADS category, lesion shape, margin, echogenicity, posterior features, pathology, and histology.
The paper introduces CAST, a concept-guided artifact suppression tuning framework that uses sparse autoencoders to identify and suppress note-specific artifacts in clinical language models. CAST labels latent features with an LLM-assisted pipeline and ICD‑10 constraints, then fine‑tunes the model while providing post‑hoc per‑concept attributions for auditability. In experiments on MIMIC‑IV discharge‑note mortality prediction, CAST outperforms standard fine‑tuned encoders and competes with strong LLM baselines while offering a feature‑level audit trail of clinical concepts and suppressed artifacts.
arXiv:2606. 19852v1 Announce Type: cross Abstract: Information extraction from pathology reports is essential for cancer staging, tumor registry population.
arXiv:2605. 30716v2 Announce Type: replace-cross Abstract: Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the complexity of case-level reasoning, where a single case may contain multiple WSIs with heterogeneous tissues and ambiguous findings.
arXiv:2607. 14116v1 Announce Type: cross Abstract: Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language.
arXiv:2607. 11257v1 Announce Type: cross Abstract: Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs.
SemEnrich introduces a self‑supervised method to enrich radiology reports by clustering sentences semantically and adding positive or neutral observations from different clusters. The enriched data consistently improves supervised fine‑tuning across multiple vision‑language metrics, with gains ranging from 3% to 7.5% on COMET, Bert, Sentence Bleu, CheXbert‑F1, and RadGraph‑F1. The authors also demonstrate that incorporating cluster information into the reward design for GRPO training yields additional performance boosts.
arXiv:2510. 15042v3 Announce Type: replace-cross Abstract: In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports.
arXiv:2606. 08945v1 Announce Type: new Abstract: We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model.
arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fa...
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 Report Supervision (R‑Super), a framework that uses radiology reports to supervise tumor segmentation models. By incorporating loss functions that align segmentation outputs with report‑derived tumor counts, sizes, and locations, R‑Super improves detection and segmentation performance. Experiments on kidney and pancreatic tumors show up to a 15% increase in F1‑Score and DSC compared to mask‑only training, outperforming methods like CLIP and multi‑task learning.