paper-with-me

Papers

Interactive and Explainable Region-guided Radiology Report Generation

2023-04-17 · CVPR 2023 1 · Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg .

📄 PDF Abstract BibTeX arXiv:2304.08295

Code (1)

ttanida/rgrg 공식 구현 pytorch

Tasks

Medical Report Generation

Similar Papers 제목 키워드 기반

Semantically Informed Salient Regions Guided Radiology Report Generation

2025-07-15 · Zeyi Hou, Zeqiang Wei, Ruixin Yan, Ning Lang 외

Recent advances in automated radiology report generation from chest X-rays using deep learning algorithms have the potential to significantly reduce the arduous workload of radiologists. However, due to the inherent mass…

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation

2024-05-23 · Zhusi Zhong, Jie Li, John Sollee, Scott Collins 외

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation an…

Image to textSentenceSurvival Prediction

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Interactive Radiology Report Retrieval

2024-01-01 · CVPR 2024 1 · Sixing Yan, William K. Cheung, Ivor W. Tsang, Keith Chiu 외

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation where irrelevant and unfaithful contents ar…

AnatomyDiagnosticRetrieval

RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance

2023-11-30 · Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab 외

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology. Such a human-in-the-loop radiology assistant could facilitat…

DiagnosticLanguage ModelingLanguage ModellingLarge Language Model+1

Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts

2024-11-16 · Yijian Gao, Dominic Marshall, Xiaodan Xing, Junzhi Ning 외

Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature su…

AnatomyDiagnosticText Generation