Skin Cancer Segmentation
8 papers with code • 2 benchmarks • 3 datasets
Libraries
Use these libraries to find Skin Cancer Segmentation models and implementations
PH2
Latest papers
Training on Polar Image Transformations Improves Biomedical Image Segmentation
marinbenc/medical-polar-training
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IEEE Access 2021
We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DebeshJha/2020-CBMS-DoubleU-Net
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The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Skin Lesion Segmentation using SegNet with Binary Cross-Entropy
In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results.
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
MaczekO/AttentionNetworkProject
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We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
LeeJunHyun/Image_Segmentation
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In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Road Extraction by Deep Residual U-Net
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
