Skin Cancer Classification

14 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Knowledge Transfer for Melanoma Screening with Deep Learning

learningtitans/isbi2017-part3 22 Mar 2017

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 8 Feb 2019

Skin cancer is by far the most common type of cancer.

Data Augmentation for Skin Lesion Analysis

fabioperez/skin-data-augmentation 5 Sep 2018

In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).

Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix 25 Apr 2019

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 19 Aug 2019

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 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

abhinavsagar/skin-cancer 14 May 2020

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 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

tbdair/fedperlv1.0 5 Mar 2021

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.

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

yhygao/Efficient_Data_Augmentation 30 Mar 2021

Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.