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Squeeze-and-Excitation Normalization for Brain Tumor Segmentation.

, , , and . BrainLes@MICCAI (2), volume 12659 of Lecture Notes in Computer Science, page 366-373. Springer, (2020)

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Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRI., , , and . BrainLes@MICCAI (2), volume 11993 of Lecture Notes in Computer Science, page 296-302. Springer, (2019)Squeeze-and-Excitation Normalization for Brain Tumor Segmentation., , , and . BrainLes@MICCAI (2), volume 12659 of Lecture Notes in Computer Science, page 366-373. Springer, (2020)Vector-based active surfaces for segmentation of dynamic PET images., , , , , and . ISBI, page 61-64. IEEE, (2013)Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning., , , , , , , , , and 37 other author(s). CoRR, (2021)4DGVF-based filtering of vector-valued images., , , , and . ICIP, page 4592-4596. IEEE, (2014)4DGVF segmentation of vector-valued images., , , , , and . ICIP, page 11-15. IEEE, (2014)Image Filtering with Advectors., , , , and . ICIP, page 1513-1517. IEEE, (2018)Pure Versus Hybrid Transformers For Multi-Modal Brain Tumor Segmentation: A Comparative Study., , , , , and . ICIP, page 1336-1340. IEEE, (2022)One-Sided Unsupervised Medical Image Synthesis with Normalized Edge Consistency., , and . ISBI, page 1-5. IEEE, (2024)Prostate Volume Segmentation in TRUS Using Hybrid Edge-Bhattacharyya Active Surfaces., , , , , , , and . IEEE Trans. Biomed. Eng., 66 (4): 920-933 (2019)