Finding-Centric Structuring of Japanese Radiology Reports and Analysis of Performance Gaps for Multiple Facilities (2025.naacl-industry)
Copied to clipboard
Yuki Tagawa, Yohei Momoki, Norihisa Nakano, Ryota Ozaki, Motoki Taniguchi, Masatoshi Hori, Noriyuki Tomiyama
| Challenge: | Despite advances in IE, radiology reports are often recorded in free-text format, limiting their secondary application. |
| Approach: | They propose a "Finding-Centric Structuring" approach which organizes reports around individual findings, facilitating secondary use. |
| Outcome: | The proposed approach organizes radiology reports around individual findings, facilitating secondary use. |
Similar Papers
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis (P18-1)
Copied to clipboard
| Challenge: | Existing methods for predicate argument structure analysis are difficult and difficult . a Japanese model can detect a zero pronoun and identify a referent of the zero pronominator . |
| Approach: | They propose a model that performs coreference resolution and predicate argument structure analysis simultaneously. |
| Outcome: | The proposed model can improve the performance of the inter-sentential zero anaphora resolution drastically. |
Structuring Radiology Reports: Challenging LLMs with Lightweight Models (2025.emnlp-main)
Copied to clipboard
Johannes Moll, Louisa Fay, Asfandyar Azhar, Sophie Ostmeier, Sergios Gatidis, Tim C. Lueth, Curtis Langlotz, Jean-Benoit Delbrouck
| Challenge: | Radiology reports lack a standardized format, limiting both interpretability and machine learning applications. |
| Approach: | They propose to use lightweight encoder-decoder models for structuring radiology reports . they compare models with eight open-source LLMs with prompting and in-context learning . |
| Outcome: | The proposed models outperform eight open-source LLMs on a human-annotated test set. |
HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)
Copied to clipboard
| Challenge: | evaluating the clinical quality of medical domain automated text generation remains a challenge. |
| Approach: | They propose a framework for histopathology automated report evaluation that prioritizes clinically relevant content by aligning critical histo pathology entities and relations between reference and generated reports. |
| Outcome: | The proposed framework outperforms existing metrics in histopathology report evaluations. |
RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports (2024.findings-acl)
Copied to clipboard
Jean-Benoit Delbrouck, Pierre Chambon, Zhihong Chen, Maya Varma, Andrew Johnston, Louis Blankemeier, Dave Van Veen, Tan Bui, Steven Truong, Curtis Langlotz
| Challenge: | RadGraph-XL is an expert-annotated dataset for structured clinical data extraction. |
| Approach: | They propose a large-scale, expert-annotated dataset for clinical entity and relation extraction using radiology reports. |
| Outcome: | The proposed model outperforms existing methods by up to 52% and outperfies GPT-4 in this domain. |
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)
Copied to clipboard
Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi
| Challenge: | Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations. |
| Approach: | They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. |
| Outcome: | The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics. |
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work on 3D radiograph report generation focuses on 2D images, but 3D medical images provide more comprehensive diagnostic information. |
| Approach: | They propose a comprehensive training recipe for building high-performing VLMs for 3DRRG using a publicly available 3D CT-report dataset. |
| Outcome: | The proposed model achieves superior performance across different model sizes and input 3D medical image resolutions. |
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026.findings-acl)
Copied to clipboard
Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
| Approach: | They propose a layman's RRG framework that leverages layperson-friendly language to enhance patient accessibility and promote robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. |
| Outcome: | The proposed framework improves model performance with more layman-style data, compared to templated professional language and inflated lexical scores. |
Harnessing PDF Data for Improving Japanese Large Multimodal Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Multimodal Models (LMMs) have demonstrated strong performance in English, but their effectiveness in Japanese remains limited due to the lack of high-quality training data. |
| Approach: | They propose a pipeline that leverages pretrained models to extract image-text pairs from PDFs . they use layout analysis, OCR, and vision-language pairing to enrich the training data . |
| Outcome: | The proposed pipeline extracts image-text pairs from Japanese PDFs, eliminating manual annotations. |
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports (2022.lrec-1)
Copied to clipboard
| Challenge: | Oftentimes, these findings and devices are referred to multiple times in a single report and are also referred across different reports of a patient. |
| Approach: | They propose a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient's radiology reports. |
| Outcome: | The proposed dataset contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies. |
Automated Structured Radiology Report Generation (2025.acl-long)
Copied to clipboard
Jean-Benoit Delbrouck, Justin Xu, Johannes Moll, Alois Thomas, Zhihong Chen, Sophie Ostmeier, Asfandyar Azhar, Kelvin Zhenghao Li, Andrew Johnston, Christian Bluethgen, Eduardo Pontes Reis, Mohamed S Muneer, Maya Varma, Curtis Langlotz
| Challenge: | Existing models struggle to produce consistent, clinically meaningful reports and standard evaluation metrics fail to capture the nuances of radiological interpretation. |
| Approach: | They propose to reformulate free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. |
| Outcome: | The proposed task reformulates free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. |