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...
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.
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: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: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...
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%.
arXiv:2609.39266v1 Announce Type: new Abstract: Fine-grained vision-language alignment in chest radiography enables zero-shot classification, grounding, and segmentation without task-specific annotat...
Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle with 3D CT imaging due to inefficient visual backbones and coarse semantic alignment. To address these issues, we propose a tailored VLP framework featuring three key components: (1) a CNN-ViT hybrid encoder that replaces ViT's patch embedding with a 3D CNN backbone to efficiently capture local anatomical details while preserving global attention and compatibility with pre-trained cross-modal priors; (2) a disease-level contrastive learning mechanism using learnable query tokens to dynamically extract disease-specific semantics from full reports and align them with corresponding visual features, thereby disentangling distinct diseases within the same anatomical region; and (3) a diagnosis-aware prompt strategy that employs real clinical phrases and aggregated disease prototypes to bridge the pre-training-inference gap and enhance zero-shot diagnostic reliability.
The paper presents a two‑stage multimodal framework for chest X‑ray interpretation that incorporates radiologist gaze data from the MIMIC‑Eye dataset. Stage 1 introduces a gaze‑token classifier that fuses image patches, bounding‑box masks, transcription embeddings, and fixation maps, and a curriculum‑scheduled loss that improves accuracy and spatial alignment, yielding a 4.4% AUC gain and 13.3% F1 improvement. Stage 2 translates classifier predictions into region‑specific diagnostic sentences using confidence‑weighted keywords, an expert dictionary, and a prompted large language model, boosting clinical‑term BERTScore and ROUGE over keyword baselines.
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.
arXiv:2606. 31099v1 Announce Type: cross Abstract: Recent years have seen substantial advances in radiology report generation (RRG), yet existing approaches predominantly adopt direct feature fusion when handling multi-view X-ray images.
arXiv:2610.29165v1 Announce Type: new Abstract: Organizing radiology reports into independently generated units offers a way to generate fine-grained clinical content and bind it to visual evidence....