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A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities
March 27, 2024, 4:46 a.m. | Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis
cs.CV updates on arXiv.org arxiv.org
Abstract: A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets. Addressing this issue, our study introduces CT-RATE, the first 3D medical imaging dataset that pairs images with textual reports. CT-RATE consists of 25,692 non-contrast chest CT volumes, expanded to 50,188 through various reconstructions, from 21,304 unique patients, along with corresponding radiology text reports. Leveraging CT-RATE, we developed CT-CLIP, a CT-focused contrastive language-image pre-training framework. As a versatile, self-supervised model, …
abstract arxiv challenge computational cs.cv dataset datasets detection foundation foundation model images imaging issue major medical medical imaging radiology rate reports research study textual type zero-shot
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