This work presents a slide-level framework generating prostate biopsy reports that is language-independent by construction: tokenizer and model are trained from scratch, and institutions can thus train native-language reporting models on their own archives.
Abstract
Prostate cancer is among the most frequently diagnosed malignancies worldwide, and structured reporting of each biopsy core burdens pathologists. Existing tools frame this as classification, leaving pathologists to assemble coherent reports, while many slide-level vision-language models rely on English-centric encoders that transfer poorly to other clinical languages. We present a slide-level framework generating prostate biopsy reports that is language-independent by construction: tokenizer and model are trained from scratch, demonstrated here in German. To address paired-data scarcity, an automated pipeline uses a locally deployed large language model to split composite reports into core-specific image-text pairs, yielding 17,344 pairs from 2,402 historical cases without manual annotation. Evaluated for clinical attributes rather than linguistic similarity, the model achieves 96.2% F1 for malignancy detection and 65.2% for Gleason grading, competitive with an FDA-cleared classifier. Grading is further validated on three external cohorts with latent-space augmentation. Institutions can thus train native-language reporting models on their own archives.
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 scarcity of large-scale WSI--report datasets and the complexity of mapping spatially distributed visual patterns to structured clinical text. To address this, we introduce a clinically curated Pan-Asia WSI--report dataset of approximately 10,500 pairs from five institutions and establish the REG 2025 benchmark through a MICCAI challenge for systematic evaluation of multimodal models. We analyze submitted methods spanning pretrained VLMs, multiple-instance learning frameworks, hierarchical expert models, retrieval-augmented generation, and cross-modal Transformers. Rather than indicating that VLM use alone was sufficient for superior performance, the results suggest that top-performing methods benefited from structured report representations, hierarchical diagnostic decomposition, and effective multimodal grounding. We identify key limitations, including instability in quantitative attribute estimation (e.g., numeric hallucination) and a tendency toward diagnostic overspecification, with some errors resembling known diagnostic pitfalls in routine pathology. These findings establish REG 2025 as a benchmark for evaluating WSI-based structured report generation and vision-language understanding in computational pathology, providing insights for the design of clinically grounded multimodal pathology models.
Yu-Mi Lee, Harim Oh, Hyo-yun Kim et al.· 0 citations
Vision-language models remain underused in colonoscopy despite the rich expert descriptions recorded in routine reports. These reports document lesion appearance, size and location but summarise entire procedures rather than caption individual frames, leaving clinical findings only weakly linked to the corresponding images. Here we develop EndoCLIP, a colonoscopy vision-language foundation model trained on 125,756 lesion-level image-text pairs progressively recovered from 280,476 routine colonoscopy records. Across lesion-level image-text retrieval, structured report generation and six multi-centre clinical classification tasks, EndoCLIP outperforms general-purpose and biomedical vision-language encoders in both zero-shot and linear-probe settings. On benign-versus-malignant classification, its linear probe approaches the performance of expert readers in a blinded study involving 12 endoscopists. These results suggest that recovering finding-to-frame correspondence can transform routine documentation into scalable supervision, enabling clinical targets to be specified in language rather than separately annotated for each task.
PURPOSE
To develop a large language model (LLM) (Truveta Language Model Oncology [TLM-Oncology]) to extract real-world oncology staging data across multiple cancer types from clinical documentation with high precision.
METHODS
We selected patients from a large integrated health system with a bladder, cervical, colorectal, breast, or prostate cancer diagnosis in their structured data. We identified relevant notes using note metadata and keywords and annotated overall stage; T, N, and M; associated timeframe; and cancer diagnosis on a sample of 700 notes as ground truth. Of the 700 notes, 450 were divided equally between training, validation, and test sets for bladder, cervical, and colorectal cancers; 150 were used for targeted error-pattern training on these cancers; and the remaining 100 were split equally between breast and prostate cancer test sets. We started with a pretrained LLM and applied supervised fine-tuning to adapt the model to structured clinical information extraction. Model performance was measured using precision, recall, and F1 scores at the relation level and individual attribute level.
RESULTS
We extracted over 2.5 million staging records for 217,768 patients from over two million notes. Relation-level precision across the six attributes ranged from 0.77 to 1.0 for the first three cancers and, without further training, 0.83 to 1.0 for two additional cancers.
CONCLUSION
TLM-Oncology extracted detailed cancer staging information for five cancers from a variety of clinical documentation within a single integrated health system with high precision and turned data that were previously inaccessible into a valuable resource for downstream use. We are currently evaluating TLM-Oncology on other solid tumors within three additional health systems to assess its generalizability.
S. Abhyankar, Rajesh Rao, Mehraveh Salehi et al.· JCO Clinical Cancer Informat...· 0 citations
Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning. Tiles from Prostate cANcer graDe Assessment (PANDA), CAncer MEtastases in LYmph nOdes challeNge 2017 (CAMELYON17), and BReAst Carcinoma Subtyping (BRACS) are used for ProsMAE pretraining to expose the encoder to diverse tissue morphology and acquisition conditions. The learned encoder is transferred for International Society of Urological Pathology (ISUP) grade classification through ProsCLS, using a frozen encoder and a linear classification head. ProsMAE achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split. Repeated-split evaluation remains necessary to further establish robustness across split compositions.
Anna Jung, Kyeonghun Kim, Youngung Han et al.· 0 citations
Domain shift remains a major obstacle to robust histopathology image segmentation, especially when models trained on several source organs are deployed to unseen anatomical sites. This study addresses cross-organ adenocarcinoma segmentation by introducing an evidence-guided vision–language segmentation framework that incorporates pathology-relevant morphological evidence into dense mask prediction. The proposed method uses a pathology vision–language encoder to extract image and text representations, a Semantic Query Booster to form image-aware segmentation queries, and an evidence-guided query recalibration that integrates positive tumor-supporting evidence and negative misleading evidence. Experiments were conducted on cross-organ adenocarcinoma datasets from the COSAS challenge under a source-only domain generalization setting, with colorectum, stomach, and pancreas as source domains and ampullary, gallbladder, and intestine as unseen target domains. The proposed framework achieved the highest pooled performance on the seen, unseen, and overall evaluation sets among the compared segmentation, domain generalization, and foundation model-based systems. These findings support the use of structured pathology evidence for cross-organ tumor segmentation under source-only training.
The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (WSIs). Human attention helps medical imaging tasks such as classification and segmentation, and becomes a strong semantic cue for identifying diagnostically informative regions for report generation. In this paper, we introduce human attention into the training of pathologist report generation models. To this end, we collected a multimodal human-attention dataset of 121 prostate WSIs annotated with pathologists'multi-scale viewport trajectories synchronized with the pathologists'verbal descriptions and cursor movements for five clinically relevant components (e.g., Gleason patterns). Using this dataset, we finetune two report generation models with an attention-alignment loss that regularizes the model attention over image patches to match the distribution of pathologist attention. We evaluate our approach on prostate cancer report generation and visual question answering using two models with different internal attention mechanisms (i.e., how image tokens are integrated into the language decoder). Experiments show average gains of 10.9% on NLP-based metrics and 19.3% in accuracy across five clinically relevant report components. Further, model attention maps extracted at inference time, with minimal computational overhead, align more closely with pathologist attention, providing stronger visual support for the generated reports by highlighting the regions that most influence the output.
Ruoyu Xue, S. Singh, Souradeep Chakraborty et al.· 0 citations