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Predicting Alzheimer's Disease and Quantifying Asymmetric Degeneration of the Hippocampus Using Deep Learning of Magnetic Resonance Imaging Data
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230830. doi: 10.1109/isbi53787.2023.10230830. Epub 2023 Sep 1.ABSTRACTIn order to quantify lateral asymmetric degeneration of the hippocampus for early predicting Alzheimer's disease (AD), we develop a deep learning (DL) model to learn informative features from the hippocampal magnetic resonance imaging (MRI) data for predicting AD conversion in a time-to-event prediction modeling framework. The DL model is trained on unilateral hippocampal data with an autoencoder based regularizer, facilitating quantification of lateral asymmetry in the hippocampal...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Xi Liu Hongming Li Yong Fan Source Type: research

Deep Clustering Survival Machines with Interpretable Expert Distributions
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230844. doi: 10.1109/isbi53787.2023.10230844. Epub 2023 Sep 1.ABSTRACTWe develop deep clustering survival machines to simultaneously predict survival information and characterize data heterogeneity that is not typically modeled by conventional survival analysis methods. By modeling timing information of survival data generatively with a mixture of parametric distributions, referred to as expert distributions, our method learns weights of the expert distributions for individual instances based on their features discriminatively such that each instance...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Bojian Hou Hongming Li Zhicheng Jiao Zhen Zhou Hao Zheng Yong Fan Source Type: research

HNAS-Reg: Hierarchical Neural Architecture Search for Deformable Medical Image Registration
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230534. doi: 10.1109/isbi53787.2023.10230534. Epub 2023 Sep 1.ABSTRACTConvolutional neural networks (CNNs) have been widely used to build deep learning models for medical image registration, but manually designed network architectures are not necessarily optimal. This paper presents a hierarchical NAS framework (HNAS-Reg), consisting of both convolutional operation search and network topology search, to identify the optimal network architecture for deformable medical image registration. To mitigate the computational overhead and memory constraints, a...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Jiong Wu Yong Fan Source Type: research

< em > Surf < /em > NN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230488. doi: 10.1109/isbi53787.2023.10230488. Epub 2023 Sep 1.ABSTRACTTo achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep learning-based framework, referred to as SurfNN, to reconstruct simultaneously both inner (between white matter and gray matter) and outer (pial) surfaces from MRIs. Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependenc...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Hao Zheng Hongming Li Yong Fan Source Type: research