Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.

Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction. Med Image Comput Comput Assist Interv. 2019 Oct;11767:149-157 Authors: Hu D, Zhang H, Wu Z, Lin W, Li G, Shen D, UNC/UMN Baby Connectome Project Consortium Abstract Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, we introduce label distribution learning (LDL), a popular machine learning paradigm focusing on the small sample problem, for infant age prediction. As directly applying LDL yields dramatically increased number of day-to-day age labels and also extremely scarce data describing each label, we propose a new strategy, called granular label distribution (GLD). Particularly, by assembling the adjacent labels to granules and designing granular distributions, GLD makes each brain MRI contribute to not only its own age but also its neighboring ages at a granule scale, which effectively keeps the information augmentation superiority of LDL and reduces the number of labels. Furthermore, to extremely augment the information supplied by the small data, we propose a novel method named granular feature distribution (GFD). GFD leverages the variability of the brain images at the same age, thus significantly increasing the learning effectiveness. Moreover, deep neural network is exploited to approximate the GLD. These strategies constitute a new m...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research