1. Single level UNet3D with multipath residual att-ention block for brain tumor segmentation;Akbar;Journal of King Saud University-Computer a-nd Information Sciences,2022
2. Advancing the cancer genome atlas glioma MRI collections with expert segmentation la-bels and radiomic features;Bakas;Scientific Data,2017
3. Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., ... & Jambawalikar, S. R. (2018b). Identifying the best machine learning algorithms for brain tumor segmentation, pr- ogression assessment, and overall survival prediction in the BRATS challenge. arXiv prep-rint arXiv:1811.02629. 10.48550/arXiv.1811.02629.
4. Fully convolutional attention network for bio-medical image segmentation;Cheng;Artificial Intelligence in Medicine,2020
5. Lung computed tomography image segme-ntation based on U-Net network fused with dilated convolution;Chen;Computer Methods and Programs in Biomedicine,2021