JOG
发表于 2025-3-23 11:25:35
Disparity Autoencoders for Multi-class Brain Tumor Segmentationluding diagnosis, monitoring, and treatment planning of gliomas. The purpose of this work was to develop a fully automated deep learning framework for multi-class brain tumor segmentation. Brain tumor cases with multi-parametric MR Images from the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Ch
overweight
发表于 2025-3-23 15:44:01
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coalition
发表于 2025-3-23 20:36:39
An Ensemble Approach to Automatic Brain Tumor Segmentationer, track tumor change, and make treatment plans. With the development of machine learning (ML)/Deep Learning (DL) image segmentation methods, the performance of medical image segmentation has significantly improved especially in terms of accuracy and time efficiency. Performance of typical deep lea
Additive
发表于 2025-3-24 01:24:33
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AWL
发表于 2025-3-24 02:27:37
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIsh of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maint
壮观的游行
发表于 2025-3-24 09:40:01
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粗语
发表于 2025-3-24 13:01:26
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Endoscope
发表于 2025-3-24 15:20:28
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Anthology
发表于 2025-3-24 20:06:50
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palliative-care
发表于 2025-3-25 01:10:45
Macroeconomics of Monetary Uniony, we utilize the idea of deep supervision for multiple depths at the decoder. We validate the MS UNet on the BraTS 2021 validation dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are ., ., and ., respectively.