CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging
This addresses the need to reduce radiologists' workload by extending automated report generation to 3D imaging, which is an incremental advancement from existing 2D methods.
The paper tackles the problem of automating radiology report generation for 3D medical imaging, specifically chest CT volumes, by introducing the first method for this task, which leverages a novel auto-regressive causal transformer and achieves effectiveness demonstrated through a baseline comparison.
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