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Benchmarks
Greatest papers with code
Knowledge Transfer for Melanoma Screening with Deep Learning
• learningtitans/isbi2017-part3
• 
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening.
IMAGE CLASSIFICATION SKIN CANCER CLASSIFICATION TRANSFER LEARNING
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).
DATA AUGMENTATION SKIN CANCER CLASSIFICATION SKIN LESION CLASSIFICATION
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.
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
ICLR 2021
• HazyResearch/model-patching
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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.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
Journal of Physics in Medicine and Biology 2020
• hasibzunair/adversarial-lesions
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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.
CONDITIONAL IMAGE GENERATION IMAGE-TO-IMAGE TRANSLATION LESION CLASSIFICATION MEDICAL IMAGE GENERATION SKIN CANCER CLASSIFICATION SKIN LESION CLASSIFICATION TRANSFER LEARNING
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.
Convolutional Neural Networks for Classifying Melanoma Images
In this work, we address the problem of skin cancer classification using convolutional neural networks.
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.
SEMANTIC SEGMENTATION SKIN CANCER CLASSIFICATION SKIN CANCER SEGMENTATION
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.