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
arXiv:2607. 19696v1 Announce Type: cross Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks.
By Duong Ngoc Vu, Hai Son Nguyen, Trong-Nghia Nguyen, Bien Tran Van, Trang Mai Xuan, Huan Vu, Thien Van Luong
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: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.
By Yucheng Chen, Jinjing Zhu, Yang Yu, Yufei Shi, Hane Naghshbandi, Jinhua Liu, Angela S. Koh, Fang Fen, Kian Eng Ong, Si Yong Yeo
arXiv:2606. 08897v1 Announce Type: cross Abstract: Spinal pathology is a leading cause of pain and disability worldwide.
By Zhiping Xiao, Junwei Yang, Gongbo Sun, Han Zhang, Hanwen Xu, Yi Yao, Zachary D. Miller, William E. King III, Mohammed M. Kanani, Jalal B. Andre, Sammy Chu, Ming Zhang, Paul E. Kinahan, Nathan M. Cross, Sheng Wang
arXiv:2605.30984v2 Announce Type: replace-cross
Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and...
By Tom Maye-Lasserre, Yitong Li, Bailiang Jian, Morteza Ghahremani, Benedikt Wiestler, Christian Wachinger
arXiv:2606. 07721v1 Announce Type: new Abstract: Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports.
By Kaouther Mouheb, Amos Pomp, Antoine Manenti, Romy de Haan, Farog Faghir, Joy Martens, Harro Seelaar, Francesco Mattace-Raso, Meike W. Vernooij, Frank J. Wolters, Stefan Klein, Esther E. Bron
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
arXiv:2609.00593v1 Announce Type: new
Abstract: Neuroradiologists rarely read a brain MRI in isolation, yet automated brain-MRI report generation has been built almost entirely for single studies. Te...
By Krish Patel, Peirong Liu
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:2607. 14581v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images for tasks such as report generation.
By Sinyoung Ra, Jonghun Kim, Hyunjin Park
arXiv:2608. 12689v1 Announce Type: cross Abstract: Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone for brain tumor diagnosis and treatment, yet current AI models face critical limitations: their lack of natural language interaction and interpretability impedes spatial information integration and cross-modal reasoning required clinically.
By Zhi Qiao, Xintong Wu, Yichu He, Feng Shi