arXiv:2608. 02615v1 Announce Type: cross Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata.
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:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
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.
MultiViewDx is a physician‑validated multimodal instruction dataset that links medical imaging studies with patient context and normalizes heterogeneous reports into an evidence‑linked workflow (evidence → findings → differential discussion → diagnosis). The dataset covers a wide range of imaging modalities and uses a unified image‑text retriever to ensure that instruction synthesis is grounded in source‑supported evidence. Fine‑tuned models on MultiViewDx achieve the highest average accuracy on four MedVQA benchmarks and receive the strongest overall rating on JAMA Clinical Challenge cases, with ablations confirming the importance of case‑level multi‑view organization and evidence‑linked reasoning.
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
arXiv:2608.22323v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus...
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: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:2607. 07673v1 Announce Type: cross Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams.
arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.
arXiv:2409.16183v2 Announce Type: replace Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...
TRACE is a deployable framework that enhances oncology language models by separating offline structure learning from online inference. It organizes oncology concepts and relations into an updatable tree‑relational structure, refines it with LM‑loss evidence, and retrieves compact prompt evidence during inference. The approach improves performance on ten classification tasks and a QA benchmark, outperforms vanilla RAG and generic GraphRAG, and provides interpretable evidence paths aligned with clinical reasoning.