arXiv Computer Vision
CPVB-RG: Cross-Prior Visual Bridging for Prior-Aware Chest X-Ray Report Generation
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CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation
arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal r...
arXiv Computation and Language
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CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation
CliQ‑RRG is a two‑stage framework for generating radiology reports that aligns visual and textual data using disease‑aware embeddings and prior‑guided attention to capture multi‑view dependencies. The second stage retrieves graph‑grounded clinical evidence and employs a large language model to produce concise, interpretable question‑answer style reports with diagnostic summaries. Experiments on two chest X‑ray benchmarks show that CliQ‑RRG outperforms previous methods on both lexical and clinical metrics, producing accurate and clinically coherent reports.
Hugging Face Trending Papers
Oct 9
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation
CliQ‑RRG is a two‑stage framework for generating radiology reports that aligns visual and textual data using disease‑aware embeddings and a Prior‑Guided Attention Module to capture multi‑view dependencies. In the second stage, GraphRAG‑guided retrieval adds graph‑grounded clinical evidence, and a large language model restructures the content into concise question‑answer pairs with diagnostic summaries. Experiments on two chest X‑ray benchmarks show that CliQ‑RRG outperforms previous methods in both lexical and clinical metrics, producing accurate and clinically coherent QA‑style reports.
arXiv AI
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CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
arXiv:2608. 03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend.
arXiv Computer Vision
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RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture
arXiv:2601.15891v4 Announce Type: replace Abstract: Vision-language pretraining has driven progress in medical image representation learning, but it depends on paired image-text data and can inherit...
arXiv Computer Vision
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ICGF-CXRMate: Image-Conditioned Clinical Context Compression and Gated Fusion for Chest X-ray Report Generation
ICGF-CXRMate is a framework that compresses heterogeneous patient data into a fixed set of clinical context tokens conditioned on chest X‑ray images. These tokens are selectively fused with visual features via gated cross‑attention and also retained in the decoder prompt, yielding shorter prompts and higher report quality. Experiments on MIMIC‑CXR and MIMIC‑IV‑ED show significant gains in RadGraph‑F1, CXR‑BERT similarity, and BLEU‑4, while reducing prompt length by 22.9%.