Skin Cancer Classification
14 papers with code • 1 benchmarks • 1 datasets
ISIC 2020 Challenge Dataset
Latest papers
Revisiting Skin Tone Fairness in Dermatological Lesion Classification
tkalbl/revisitingskintonefairness
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Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones.
SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification
longman-stan/skindistilvit
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By adding classification heads at each level of the transformer and employing a cascading distillation process, we improve the balanced multi-class accuracy of the base model by 2. 1%, while creating a range of models of various sizes but comparable performance.
Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection
This paper challenges this notion and argues that mole size, a critical attribute in professional dermatology, can be misleading in automated melanoma detection.
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
pbevan1/Skin-Deep-Unlearning
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Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible.
Soft-Attention Improves Skin Cancer Classification Performance
skrantidatta/Attention-based-Skin-Cancer-Classification
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Soft-Attention mechanism enables a neural network toachieve this goal.
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.
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.
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.
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.
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.
