CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging
Ibrahim Ethem Hamamci Sezgin Er Bjoern Menze
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. Access our code at:
https://github.com/ibrahimethemhamamci/CT2Rep