arXiv Computer Vision
Entity-Constrained CBCT Retrieval for Low-Resource Dental Record Completion
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Clinical Reasoning Under a Partially Observed Objective in Cone Beam CT Report Generation
arXiv:2609.13238v1 Announce Type: new Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large languag...
Hugging Face Trending Papers
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CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations. Vision-language models offer a natural interface for text-to-image retrieval, but current biomedical models are primarily optimized for report-to-image matching rather than for satisfying short clinical search queries.
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Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).
arXiv Computation and Language
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Occlusal Geometry in Closed Form for Orthodontic Report Generation
arXiv:2609.13237v1 Announce Type: cross Abstract: Orthodontic report generation from intraoral data is normally cast as multimodal captioning, yet the released Bite2Text scan pairs are supplied alrea...
arXiv AI
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Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
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MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis
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.