VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912192776896512 |
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| author | Liu, Zelong Tieu, Andrew Patel, Nikhil Soultanidis, Georgios Deyer, Louisa Wang, Ying Huver, Sean Zhou, Alexander Mei, Yunhao Fayad, Zahi A. Deyer, Timothy Mei, Xueyan |
| author_facet | Liu, Zelong Tieu, Andrew Patel, Nikhil Soultanidis, Georgios Deyer, Louisa Wang, Ying Huver, Sean Zhou, Alexander Mei, Yunhao Fayad, Zahi A. Deyer, Timothy Mei, Xueyan |
| contents | Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present VIsualization and Segmentation Masked AutoEncoder (VIS-MAE), novel model weights specifically designed for medical imaging. Specifically, VIS-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET,X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VIS-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications. In addition, VIS-MAE has improved label efficiency as it can achieve similar performance to other models with a reduced amount of labeled training data (50% or 80%) compared to other pre-trained weights. VIS-MAE represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload. The source code of this work is available at https://github.com/lzl199704/VIS-MAE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_01034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification Liu, Zelong Tieu, Andrew Patel, Nikhil Soultanidis, Georgios Deyer, Louisa Wang, Ying Huver, Sean Zhou, Alexander Mei, Yunhao Fayad, Zahi A. Deyer, Timothy Mei, Xueyan Image and Video Processing Computer Vision and Pattern Recognition Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present VIsualization and Segmentation Masked AutoEncoder (VIS-MAE), novel model weights specifically designed for medical imaging. Specifically, VIS-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET,X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VIS-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications. In addition, VIS-MAE has improved label efficiency as it can achieve similar performance to other models with a reduced amount of labeled training data (50% or 80%) compared to other pre-trained weights. VIS-MAE represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload. The source code of this work is available at https://github.com/lzl199704/VIS-MAE. |
| title | VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.01034 |