A Unified Polyhedral Representation (UPER) is introduced to model multi-dimensional dMRI data across over 10 unique shells, sourced from 2800 scans across 17 datasets, and is the first deep learning model capable of modeling varying radial and angular spaces, and accommodating diverse shell configurations.
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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