CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging
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Hamamci et al.'s customized transformer model required 7 days of training on a single NVIDIA A100 GPU.
“However, their customized transformer model required 7 days of training on a single NVIDIA A100 GPU, whereas our BrainGPT model requires only 12 h of fine-tuning on two NVIDIA A100 GPUs.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [35]
Generative models are competent in 3D chest CT report generation at the state-of-the-art level (BLEU-1 = 46, BLEU-4 = 36.9, METEOR = 29.5, ROUGE-L = 45.9).
“Recent work by Hamamci et al. revealed that generative models are competent in 3D chest CT report generation at the state-of-the-art (SOTA) level (BLEU-1 = 46, BLEU-4 = 36.9, METEOR = 29.5, ROUGE-L = 45.9)35–37.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [35]
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- Authors
- Hamamci, I. E., Er, S., & Menze, B.
- Affiliations
- Istanbul Medipol University (TR)
- University of Zurich (CH)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 476-486
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4403650369
- Cited by
- 47
- References
- 25
- Citations by year
- 2026: 11 · 2025: 36
- FWCI
- 9.73
- Type
- conference paper
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- automated radiology report generation, 3D medical imaging, chest CT, cross-attention, multimodal fusion, hierarchical memory, radiologist workload reduction
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