arXiv Computer Vision

ViBMR: A Large-Scale Multi-Sequence Brain MRI Dataset and Comprehensive Evaluation for Vietnamese Radiology Report Generation

ViBMR is the first large‑scale multi‑sequence brain MRI dataset paired with routine Vietnamese clinical reports, containing 3,649 patients and 11,875 MRI series across T1, T2, FLAIR, and contrast‑enhanced T1 sequences. The authors establish a comprehensive evaluation framework for brain MRI report generation, testing various medical vision‑language models, multi‑sequence inputs, and feature aggregation strategies. Results highlight dataset characteristics, model behavior, and the importance of native low‑resource clinical datasets for reliable Vietnamese‑origin brain MRI report generation.

arXiv AI
Oct 1

GateSPINE: Gated Cross-View Fusion for Lumbar Spine MRI Report Generation

GateSPINE is a vision‑language framework designed for automated lumbar spine MRI report generation. It fuses sagittal T1 and T2 volumes using a training‑free gated cross‑view fusion module, then encodes the fused sagittal and axial volumes with parallel 3D encoders before decoding the combined representation into a report. Evaluated on three datasets, GateSPINE achieves the highest clinical efficacy F1 scores, improving recall across all datasets while remaining competitive on standard natural language generation metrics.

By Hoang Nguyen Van, Cuong Vuong Tuan, Trang Mai Xuan, Bien Tran Van, Nam Tran Van, Thien Van Luong
Hugging Face Trending Papers
Jul 22

PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis

The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks. In this study, we present PhenSPINE, a Magnetic Resonance Imaging dataset comprising 16,813 images from 250 patients, curated to facilitate advanced deep learning research.

arXiv AI
Aug 24

ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora

ASTAR is an LLM-based framework that automatically generates standardized radiology reporting templates from large-scale clinical free-text corpora, eliminating the manual, expert-driven template construction process. In experiments on 4,215 fetal brain MRI reports from multiple centers, ASTAR‑induced templates outperformed two expert‑curated templates in template coverage, information fidelity, diagnostic fidelity, and expert‑rated usability. The approach reduces template development time from weeks of committee deliberation to hours of automated processing.

By Xinfeng Zhang, Mingxuan Liu, Yifei Chen, Juncheng Zhu, Kasidit Anmahapong, Yiming Huang, Yuan Zhang, Hongjia Yang, Yi Liao, Gang Ning, Haibo Qu, Qiyuan Tian
arXiv AI
Jun 2

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.

By Zixian Su, Hongkai Zhang, Fan Gao, Encheng Su, Taiping Qu, Jingwei Guo, Nan Zhang, Hui Wang, Zhen Zhou, Kairui Bo, Yan Chen, Yue Ren, Shuai Li, Lei Xu, Henggui Zhang
arXiv Machine Learning
Jun 25

Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation

arXiv:2411. 15490v2 Announce Type: replace-cross Abstract: Acute ischemic stroke (AIS) requires time-critical decision-making, where inaccurate interpretation of neuroimaging findings can lead to irreversible disability.

By Junhyeok Lee, Yujin Oh, Dahyoun Lee, Hyon Keun Joh, Chul-Ho Sohn, Sung Hyun Baik, Cheol Kyu Jung, Jung Hyun Park, Kyu Sung Choi, Byung-Hoon Kim, Jong Chul Ye