By: Oren Avram, Berkin Durmus, Nadav Rakocz, Giulia Corradetti, Ulzee An, Muneeswar G. Nittala, Prerit Terway, Akos Rudas, Zeyuan Johnson Chen, Yu Wakatsuki, Kazutaka Hirabayashi, Swetha Velaga, Liran Tiosano, Federico Corvi, Aditya Verma, Ayesha Karamat, Sophiana Lindenberg, Deniz Oncel, Louay Almidani, Victoria Hull, Sohaib Fasih-Ahmad, Houri Esmaeilkhanian, Maxime Cannesson, Charles C. Wykoff, Elior Rahmani, Corey W. Arnold, Bolei Zhou, Noah Zaitlen, Ilan Gronau, Sriram Sankararaman, Jeffrey N. Chiang, Srinivas R. Sadda, and Eran Halperin
The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities. The model, which we named SLIViT (for ‘slice integration by vision transformer’), preprocesses a given volumetric scan into 2D images, extracts their feature map, and integrates it into a single prediction.
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