Skin Lesion Segmentation
73 papers with code • 3 benchmarks • 2 datasets
ISIC2016
University of Waterloo skin cancer database
Most implemented papers
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
nabsabraham/focal-tversky-unet
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We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
RECOD Titans at ISIC Challenge 2017
learningtitans/isbi2017-part3
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This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017).
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
rezazad68/BCDU-Net
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In Proceedings of the IEEE/CVF international conference on computer vision workshops 2019
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
HiLab-git/CA-Net
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Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.
Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation
The segmentation of skin lesions is a crucial task in clinical decision support systems for the computer aided diagnosis of skin lesions.
Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging
We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images.
DSNet: Automatic Dermoscopic Skin Lesion Segmentation
We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2.
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis.
Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle
WuJunde/MrPrism
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Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty.
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets
siyi-wind/mdvit
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Naivly combining datasets from different domains can result in negative knowledge transfer (NKT), i. e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity.
