M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Abstract
In this study, we propose a three-dimensional Medical image classifier using Multi-plane and Multi-slice Trans-former (M3T) network to classify Alzheimer's disease (AD) in 3D MRI images. The proposed network synergically com-bines 3D CNN, 2D CNN, and Transformer for accurate AD classification. The 3D CNN is used to perform natively 3D representation learning, while 2D CNN is used to utilize the pre-trained weights on large 2D databases and 2D repre-sentation learning. It is possible to efficiently extract the lo-cality information for AD-related abnormalities in the local brain using CNN networks with inductive bias. The trans-former network is also used to obtain attention relationships among multi-plane (axial, coronal, and sagittal) and multi-slice images after CNN. It is also possible to learn the ab-normalities distributed over the wider region in the brain using the transformer without inductive bias. In this ex-periment, we used a training dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which contains a total of 4,786 3D T1-weighted MRI images. For the validation data, we used dataset from three different institutions: The Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL), The Open Access Series of Imaging Studies (OASIS), and some set of ADNI data indepen-dent from the training dataset. Our proposed M3T is compared to conventional 3D classification networks based on an area under the curve (AUC) and classification accuracy for AD classification. This study represents that the pro-posed network M3T achieved the highest performance in multi-institutional validation database, and demonstrates the feasibility of the method to efficiently combine CNN and Transformer for 3D medical images.
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SAM has achieved impressive/promising zero-shot segmentation performance on a variety of natural image datasets.
“The recently developed Segment Anything Model is a foundation model that has achieved promising zero-shot segmentation performance on a variety of natural image datasets [18].”
From Segment anything model for medical image analysis: An experimental study (2023) · cites this paper as [18]
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Segment anything model for medical image analysis: An experimental study
Mazurowski, M. A., Dong, H., Gu, H., Yang, J., Konz, N., & Zhang, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102918 · Cited by 802 · see citing works
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- Authors
- Jang, J., & Hwang, D.
- Affiliations
- Yonsei University (KR)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 20686-20697
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312749457
- Cited by
- 133
- References
- 73
- Citations by year
- 2026: 19 · 2025: 51 · 2024: 34 · 2023: 26 · 2022: 3
- FWCI
- 63.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Samsung Electronics (SRFC-TF2103-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- Alzheimer's disease classification, 3D MRI, CNN-Transformer hybrid, 3D convolutional neural networks, T1-weighted imaging, ADNI, AIBL, OASIS
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