Dec 28, 2024 · Our research seeks to evaluate STU-Net's efficacy and transferability on the BraTS23 dataset, encapsulating diverse MRI scans of brain tumor patients.
Dec 28, 2024 · Being one of the most extensive medical image segmentation models, STU-Net's sizes range between 14 million to 1.4 billion parameters. Our ...
Our research seeks to evaluate STU-. Net's efficacy and transferability on the BraTS23 dataset, encapsulating diverse MRI scans of brain tumor patients. The ...
Our research seeks to evaluate STU-Net's efficacy and transferability on the BraTS23 dataset, encapsulating diverse MRI scans of brain tumor patients. The code ...
Apr 13, 2023 · In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 ...
Brain Tumor Segmentation, and Cross-Modality Domain Adaptation for ...
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Jan 3, 2025 · The AGU-Net Architecture for Brain Tumor Segmentation: BraTS Challenges 2023 ... Evaluating STU-Net for Brain Tumor Segmentation · Ziyan Huang, ...
Aug 1, 2023 · We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain ...
Missing: STU- | Show results with:STU-
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Jul 25, 2024 · Stu-net: Scalable and transfer- able medical image segmentation models empowered by large-scale supervised pre- training. arXiv preprint arXiv: ...
In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available.
Current practices for the evaluation of deep learning models for brain tumor segmentation did not reflect the segmentation quality perception of clinical ...