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2 papers
Recent·Most cited
G. K. Murugesan, D. McCrumb, E. Brunner, J. Kumar, R. Soni, V. Grigorash, A. Chang, A. Peck, J. VanOss, S. Moore
This paper develops a residual UNet model for abdominal multi-organ segmentation using CT and MRI scans, achieving high Dice scores in held-out tests.
bioRxiv · Feb 15, 2023 · PreprintBioengineering
G. K. Murugesan, D. Mccrumb, E. Brunner, J. Kumar, R. Soni, V. Grigorash, A. Chang, J. VanOss, S. Moore
The paper develops a deep learning method using residual UNET and adaptive ensemble for automated FDG PET/CT lesion segmentation, achieving a dice score of 0.5541 on a test dataset.
bioRxiv · Feb 6, 2023 · PreprintBioengineering