Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI

By Chunfeng Lian, Mingxia Liu, Jun Zhang, Dinggang Shen. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018

490 citations recorded by the source corpus.

Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.

Subfields: Biomedical Engineering, Complementary and alternative medicine, Neurology

View Paper: https://www.semanticscholar.org/paper/261507db4679ebe05d2c5e797cacc0ba8de2f9e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Neurology

Citations in the corpus

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “(Lian et al., 2018) ACC=0.”
Deep Bayesian Hashing with Center Prior for Multi-modal Neuroimage Retrieval (2020, methodology): “The architecture of modality-speciﬁc networks for sMRI and PET ( i . e ., X and Y ) are similar to the 3DCNN model designed in [16], with the last fully-connected (FC) layer replaced by a new FC layer with k units as the hash layer.”
TransMorph: Transformer for unsupervised medical image registration (2021, unknown): “…challenge of 2015 (Ronneberger et al., 2015), ConvNet-based methods have become a major focus of attention in medical image analysis fields, such as tumor segmentation (Isensee et al., 2021; Zhou et al., 2019), image reconstruction (Zhu et al., 2018), and disease diagnostics (Lian et al., 2018).”