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Ggad-Net: A Global Gate Axial-Attention in Double U-Net for Skin Lesion Segmentation

https://doi.org/10.2139/ssrn.4744729
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UNet++: A Nested U-Net Architecture for Medical Image Segmentation
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Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
inferredcites →https://doi.org/10.1007/978-3-030-58548-8_7openalex-extracted
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
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Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-87589-3_28openalex-extracted
U-Net Transformer: Self and Cross Attention for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-031-25066-8_9openalex-extracted
Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-extracted
U-Net: Convolutional Networks for Biomedical Image Segmentation
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Overview of deep learning in medical imaging
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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
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An overview of deep learning in medical imaging
inferredcites →https://doi.org/10.1016/j.neunet.2019.08.025openalex-extracted
MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation
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A survey of loss functions for semantic segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-extracted
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/itme.2018.00080openalex-extracted
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
inferredcites →https://doi.org/10.1109/jbhi.2016.2636665openalex-extracted
Deep Learning for Health Informatics
inferredcites →https://doi.org/10.1109/lgrs.2018.2802944openalex-extracted
Road Extraction by Deep Residual U-Net
inferredcites →https://doi.org/10.1109/tpami.2021.3059968openalex-extracted
Image Segmentation Using Deep Learning: A Survey
inferredcites →https://doi.org/10.1109/tpami.2024.3435571openalex-extracted
Medical Image Segmentation Review: The Success of U-Net
inferredcites →https://doi.org/10.1145/2964284.2967274openalex-extracted
UnitBox
inferredcites →https://doi.org/10.1609/aaai.v36i3.20144openalex-extracted
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.45openalex-extracted
Dice Loss for Data-imbalanced NLP Tasks
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-extracted
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1212.5701openalex-extracted
ADADELTA: An Adaptive Learning Rate Method
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
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An overview of gradient descent optimization algorithms
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The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-extracted
Attention U-Net: Learning Where to Look for the Pancreas
inferredcites →https://doi.org/10.48550/arxiv.1906.05909openalex-extracted
Stand-Alone Self-Attention in Vision Models
inferredcites →https://doi.org/10.48550/arxiv.2109.07162openalex-extracted
MISSFormer: An Effective Medical Image Segmentation Transformer
inferredcites →https://doi.org/10.48550/arxiv.2310.07781openalex-extracted
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
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