Skin Cancer Classification
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Skin Cancer Classification
ISIC 2020 Challenge Dataset
Most implemented papers
Knowledge Transfer for Melanoma Screening with Deep Learning
learningtitans/isbi2017-part3
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Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening.
Skin Lesion Synthesis with Generative Adversarial Networks
alceubissoto/gan-skin-lesion
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Skin cancer is by far the most common type of cancer.
Data Augmentation for Skin Lesion Analysis
fabioperez/skin-data-augmentation
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In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
briansukhnandan/Skin-Lesion-Classifier-CNN
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The best ROC AUC values for melanoma and basal cell carcinoma are 94. 40% (ResNet 152) and 99. 30% (DenseNet 201) versus 82. 26% and 88. 82% of dermatologists, respectively.
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.
Deep neural network or dermatologist?
KyleYoung1997/DNNorDermatologist
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We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
hasibzunair/adversarial-lesions
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Journal of Physics in Medicine and Biology 2020
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
Convolutional Neural Networks for Classifying Melanoma Images
In this work, we address the problem of skin cancer classification using convolutional neural networks.
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
HazyResearch/model-patching
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ICLR 2021
Particularly concerning are models with inconsistent performance on specific subgroups of a class, e. g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage.
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1. 8% and 15. 8%, respectively.
