AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework
Author:
Funder
Research Center of the College of Computer and Information Sciences, King Saud University
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09209-1.pdf
Reference60 articles.
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2. Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693
3. Liu L, Zhang Z, Li S, Ma K, Zheng Y (2021) S-cuda: self-cleansing unsupervised domain adaptation for medical image segmentation. Med Image Anal 74:102214
4. Fu S, Lu Y, Wang Y, Zhou Y, Shen W, Fishman W, Yuille A (2020) Domain adaptive relational reasoning for 3d multi-organ segmentation. In: Medical image computing and computer assisted intervention—MICCAI 2020: 23rd international conference, Lima, Peru, October 4–8, 2020, proceedings, Part I, vol 23. Springer, pp 656–666
5. Zemmal N, Azizi N, Dey N, Sellami M (2016) Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. J Med Imaging Health Inform 6(1):53–62
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