bibtext: @InProceedings{ Ham_CT2Rep_MICCAI2024,
                   author = { Hamamci, Ibrahim Ethem and Er, Sezgin and Menze, Bjoern },
                   title = { { CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report generation for 2D medical imaging, extending this to 3D has been unexplored due to computational complexity and data scarcity. We introduce the first method to generate radiology reports for 3D medical imaging, specifically targeting chest CT volumes. Given the absence of comparable methods, we establish a baseline using an advanced 3D vision encoder in medical imaging to demonstrate our method’s effectiveness, which leverages a novel auto-regressive causal transformer. Furthermore, recognizing the benefits of leveraging information from previous visits, we augment CT2Rep with a cross-attention-based multi-modal fusion module and hierarchical memory, enabling the incorporation of longitudinal multimodal data.
title: CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging
authors: Hamamci, Ibrahim Ethem Er, Sezgin Menze, Bjoern