TY  - JOUR
A1  - Liang, Shuang
A1  - Gu, Yu
T1  - Computer-aided diagnosis of Alzheimer’s disease through weak supervision deep learning Framework with attention mechanism
T2  - Sensors
N2  - Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.
KW  - Alzheimer’s disease
KW  - attention module
KW  - CNN
KW  - computer-aided diagnosis
KW  - magnetic resonance imaging
KW  - multi-task learning
KW  - weakly supervised learning
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69260
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-692603
SN  - 1424-8220
N1  - This research was funded by the Ministry of Science and Technology of the People’s Republic of China (Grant No. 2017YFB1400100) and the National Natural Science Foundation of China (Grant No. 61876059).
VL  - 21
IS  - 1, art. 220
SP  - 1
EP  - 15
PB  - MDPI
CY  - Basel
ER  -