Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.04159

Diagnosis-Conditioned Spatial Gating and Decoder-Level Supervised Contrastive Learning for Radiology Report Generation

Md Mustafizur Rahman·Mylene C. Q. Farias
View original source ↗Read source document ↗Export metadata ↓

Abstract

Radiology report generation models can produce fluent text while still containing finding-level inaccuracies. Diagnosis-driven methods improve generation by conditioning on predicted findings, but these predictions do not directly modify the visual patch features provided to the decoder, and global gating applies the same modulation across spatial locations. We introduce a Position-Aware Gate (PAG) that uses predicted finding representations to modulate visual patches spatially without region supervision. We also propose a Decoder-level Supervised Contrastive Loss (DSCL) that structures decoder representations using shared positive findings rather than instance identity. On MIMIC-CXR, PAG+DSCL improves clinical efficacy (CE) F1 from 0.484 to 0.502 over a matched global-gate reference, while PAG and DSCL individually reach 0.491 and 0.495. Without additional fine-tuning, the combined model achieves 0.226 CE F1 on IU X-Ray, compared with 0.211 reported by PromptMRG.

Explore related subjects

cs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md Mustafizur Rahman, Mylene C. Q. Farias. 2026-10-03. Diagnosis-Conditioned Spatial Gating and Decoder-Level Supervised Contrastive Learning for Radiology Report Generation. https://arxiv.org/abs/2610.04159

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Deep Learning Reforms Image Matching: A Survey and Outlook

Image matching, which establishes correspondences between two images to recover 3D structure and camera geometry, is a cornerstone of computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines, composed of a detector-descriptor, a feature matcher, an outlier filter, and a geometric estimator, falter in challenging scenarios. Recent advances in deep learning have substantially improved both their robustness and accuracy. This survey reviews how deep learning has progressively transformed the classical image matching pipeline. Our taxonomy is aligned with the traditional pipeline and covers two aspects: i) replacing individual steps with learnable alternatives, including learnable detector-descriptors, outlier filters, and geometric estimators; and ii) merging multiple steps into end-to-end learnable modules, including middle-end sparse matchers, end-to-end semi-dense/dense matchers, and pose regressors. We first examine the design principles, advantages, and limitations of both aspects, and then benchmark representative methods on relative pose estimation, homography estimation, matching accuracy assessment, visual localization, and 3D reconstruction. Finally, we discuss open challenges and directions for future research. By systematically categorizing and evaluating learning-based methods, this survey offers a clear overview of how image matching is evolving and where further progress is needed. The project repository is available at https://github.com/ZizhuoLi/awesome-image-matching-survey.

cs.CV↗
arXiv2026

RefAtomNet++: Advancing Referring Atomic Video Action Recognition using Multi-Trajectory Semantic Retrieval

Who is being described, where are they, and what are they doing? Referring Atomic Video Action Recognition (RAVAR) answers these questions jointly by grounding a natural-language reference to a person and recognizing that person's fine-grained atomic actions in complex multi-person videos. Progress in RAVAR is constrained by limited benchmark scale and weak alignment between fine-grained linguistic and scene cues and temporally coherent visual cues. We address both challenges with a new dataset and model. We introduce RefAVA++, a large-scale dataset comprising 2,950,920$ frames, 75,111 annotated person instances, and 80 atomic action categories. Its references describe appearance and spatial attributes while deliberately omitting action labels, requiring models to infer actions directly from visual cues. We further propose RefAtomNet++, which models complementary semantics at the holistic-sentence, partial-keyword, and scene-attribute levels. Fine-grained semantic cues retrieve aligned visual tokens across time to construct trajectories, which are aggregated through Mamba-based state-space modeling and fused using multi-hierarchical semantic-aligned cross-attention. This design enables accurate joint person localization and multi-label atomic action recognition. Equipped with the InternVideo2.5 backbone, RefAtomNet++ achieves 50.39%/51.14% mIoU, 59.21%/59.55% mAP, and 73.89%/76.59% AUROC on RefAVA, and 49.49%/50.33% mIoU, 62.33%/61.90% mAP, and 76.06%/76.10% AUROC on RefAVA++ validation/test sets, respectively. The dataset and code are available at https://github.com/KPeng9510/refAVA2.

cs.CV↗
arXiv2026

3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models

Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability mismatch reveals a critical ``spatial intelligence gap,'' where models fail to construct coherent 3D mental representations from 2D observations. We uncover this gap via diagnostic analyses showing the bottleneck is a missing view-consistent spatial interface rather than insufficient visual features or weak reasoning. To bridge this, we introduce \textbf{3ViewSense}, a framework that grounds spatial reasoning in Orthographic Views. Drawing on engineering cognition, we propose a ``Simulate-and-Reason'' mechanism that decomposes complex scenes into canonical orthographic projections to resolve geometric ambiguities. By aligning egocentric perceptions with these allocentric references, our method facilitates explicit mental rotation and reconstruction. Empirical results on spatial reasoning benchmarks demonstrate that our method significantly outperforms existing baselines, with consistent gains on occlusion-heavy counting and view-consistent spatial reasoning. The framework also improves the stability and consistency of spatial descriptions, offering a scalable path toward stronger spatial intelligence in multimodal systems.~\footnote{https://github.com/Jasaxion/3ViewSense}

cs.CV↗