Medical computer vision presents unique challenges compared to natural images, largely due to the high dimensionality of the data (e.g., 3D and 4D imaging) and limited training samples. Certain medical imaging modalities, such as Diffusion-weighted Magnetic Resonance Imaging (dMRI) - a critical tool for examining brain...
Tian-Yuan Yao, Zhi-Yuan Li, Michael E. Kim et al.· Medical Image Analysis· 0 citations
Abstract. Purpose Normative modeling of quantitative brain measurements is being widely adopted as a promising method for charting population-level developmental trajectories and identifying abnormalities in groups and individuals. Generalized additive models for location, scale, and shape (GAMLSS) are a statistical fr...
Michael E. Kim, G. Rudravaram, Adam M. Saunders et al.· Journal of Medical Imaging· 0 citations
GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs, is introduced, showing that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.
V. Bashyam, G. Erus, Jun-Hao Wen et al.· 0 citations
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