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Ish: Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, Thomas Brox
BobOchiq kirish201545 iqtibosABInnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl +2
MaqolaOchiq kirish202012 iqtibosABIEncoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou +2
BobOchiq kirish201811 iqtibosABIBrain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging
Akmalbek Abdusalomov, Mukhriddin Mukhiddinov, Taeg Keun Whangbo
MaqolaOchiq kirish202310 iqtibosABIResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Foivos I. Diakogiannis, François Waldner, Peter Caccetta +1
MaqolaOchiq kirish20205 iqtibosABIA deep learning approach for brain tumor classification using MRI images
Muhammad Aamir, Zia-ur Rahman, Zaheer Ahmed Dayo +8
MaqolaOchiq kirish20222 iqtibosABIRobust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor
Atika Akter, Nazeela Nosheen, Sabbir Ahmed +5
MaqolaOchiq kirish20232 iqtibosABI