Segmentation of Multiple Sclerosis Using Autoencoder and Classifier
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8438-1_9
Reference21 articles.
1. Sadeghibakhi M, Pourreza H, Mahyar H (2022) Multiple sclerosis lesions segmentation using attention-based CNNs in FLAIR images. IEEE J Transl Eng Health Med 10:1–11
2. Yoo Y, Brosch T, Traboulsee A, Li DKB, Tam R (2014) Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In: Wu G, Zhang D, Zhou L (eds) Machine learning in medical imaging. MLMI 2014. Lecture notes in computer science, vol 8679. Springer, Cham
3. Jain S, Rajpal N, Yadav J (2022) Supervised and unsupervised machine learning techniques for multiple sclerosis identification: a performance comparative analysis. https://doi.org/10.1007/978-981-16-3346-1-30
4. Alrabai A, Echtioui A, Hamida A (2022) Multiple sclerosis segmentation using deep learning models: comparative study. https://doi.org/10.1109/ATSIP55956.2022.9805983
5. McKinley R, Wepfer R, Aschwanden F et al (2021) Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci Rep 11:1087
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