Journal ArticleDOI
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
TLDR
Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.Abstract:
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.read more
Citations
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Applications of Deep Learning and Reinforcement Learning to Biological Data
TL;DR: This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data and compares the performances of DL techniques when applied to different data sets across various application domains.
Journal ArticleDOI
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.
Junhao Wen,Elina Thibeau-Sutre,Mauricio Diaz-Melo,Jorge Samper-González,Alexandre Routier,Simona Bottani,Didier Dormont,Stanley Durrleman,Ninon Burgos,Olivier Colliot +9 more
TL;DR: The open-source framework for classification of AD using CNN and T1-weighted MRI is extended and found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance.
Journal ArticleDOI
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
TL;DR: A hierarchical fully convolutional network (H-FCN) is proposed 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.
Proceedings ArticleDOI
Thoracic Disease Identification and Localization with Limited Supervision
TL;DR: In this article, a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images is presented, which can effectively leverage both class information as well as limited location annotation.
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