A simple self-supervised learning framework with patch-based data augmentation in diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much a...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Biomedical signal processing and control Ročník 96; s. 106572
Hlavní autori:	Gong, Haoqiang, Wang, Zhiwen, Huang, Shuaihui, Wang, Jinfeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2024
Predmet:	Alzheimer’s disease Attention mechanism Data augmentation MRI brain patch Self-supervised learning Attention mechanism Data augmentation Alzheimer’s disease Self-supervised learning MRI brain patch
ISSN:	1746-8094
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much attention due to their advantages of no manual labeling, so they are very suitable for solving the problems of difficulty in data acquisition and high cost of manual labeling in medical image processing. However, the existing self-supervised algorithms applied to medical images often have poor diagnostic effects and consume large resources, as a consequence, the model’s ability to learn meaningful representations from medical images is hampered, leading to suboptimal performance. Subsequently, diverse patches extracted from the same brain are amalgamated to create contrast views, and attention weights are then employed to enhance the fitting and generalization capacity of the model. Experimental results on the ADNI dataset with 1365 subjects show that PD-SIM has been improved in the diagnosis of different diseases(such as the classification ACC of AD and CN reached 0.797, and the classification ACC of early cognitive impairment reached 0.7036), and also alleviates the problem of large consumption of computer resources, downstream tasks require only 52ms per image. The proposed method performs well in atrophic structure identification and AD diagnosis. Therefore, PD-SIM has a wide range of application prospects and is of great practical significance within the realm of medical image analysis. The data and code can be found at https://github.com/Z1Ting/PD-SIM. •A simple contrastive learning framework is proposed to recombine local microstructures into global features.•A patch-based strategy is introduced to filter the normal regions and generate multiple key local regions.•The spatial attention mechanism is integrated to make the model pay better attention to the lesion area.
ISSN:	1746-8094
DOI:	10.1016/j.bspc.2024.106572