Fully convolutional networks for multi-modality isointense infant brain image segmentation

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Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology

Ruan, D., Zhang, X., Yuan, Z., Lu, Z., Xia, L., & Jiang, M. (2026) · Journal of Imaging · doi:10.3390/jimaging12060269

MRI Brain Tumor Classification using Patch-Level Graph Attention Networks

Maheswari, K. P., & Kalaiselvi, T. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00144-5

Pediatric Brain Tumor Segmentation in MRI using Residual U-Net with scSE Attention Networks

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Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817

3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation

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Multi-stream 3D FCN with multi-scale deep supervision for multi-modality isointense infant brain MR image segmentation

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A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation

Li, T., Zhou, F., Zhu, Z., Shu, H., & Hongtu Zhu, B. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363668 · Cited by 9

Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

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