arXiv AI
Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
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arXiv Machine Learning
Oct 23
Diagnostic Loss: Asymmetric Supervision for Radiology Report Generation
The paper introduces Diagnostic Loss (DLoss), an objective designed to align radiology report generation with the asymmetric requirement of accurately stating supported diagnoses while avoiding unsupported ones. DLoss operates on decoder token probabilities, aggregating mass over vocabularies for supported and unsupported diagnoses and applying asymmetric pooling to encourage correct diagnoses and suppress spurious ones. Applied to various 3D MedVLMs on 50,000 abdominal CT scans, DLoss improves diagnostic faithfulness without compromising conventional language‑generation metrics.
arXiv Computer Vision
Oct 7
Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation
The paper identifies a representational bottleneck in 3D CT embeddings that limits pathology coverage in automated radiology reports. It introduces AdaRAG-CT, an adaptive retrieval-augmented framework that supplements visual features with controlled textual retrieval to improve report generation. On the CT-RATE benchmark, AdaRAG-CT achieves state‑of‑the‑art clinical efficacy, raising Clinical F1 from 0.420 to 0.480.
arXiv Computation and Language
Sep 7
CT-$\Delta$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models
CT‑ΔBench is a new benchmark designed to evaluate vision‑language models on longitudinal 3D medical imaging difference reporting. It provides patient‑level split data, change‑aware metrics, and physician‑validated references to assess clinically meaningful interval changes between two CT scans. The paper also introduces DeltaMed, a baseline model that directly reasons over paired CT scans, and compares it to an indirect two‑stage approach that first generates single‑timepoint reports before differencing.
arXiv Computer Vision
Sep 2
Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT
Instance-Guided Report Anchoring (IGRA) is a model-agnostic module that links each abnormality instance in a chest CT to the corresponding finding in a radiology report during training, while discarding text components at inference. By reformulating free-text grounding as multi-label volumetric segmentation, IGRA allows all abnormality categories to be predicted in a single image-only forward pass. The method improves Dice scores by 22.5% over the strongest image-only baseline and matches state‑of‑the‑art performance on single-finding subsets, with consistent gains across multiple 3D segmentation backbones and datasets.
Hugging Face Trending Papers
Jun 24
Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
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.
arXiv AI
Aug 5
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.