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Greatest papers with code
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 Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
On the official test set our method is ranked first for both tasks with a balanced accuracy of 63. 6% for task 1 and 63. 4% for task 2.
Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification.
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 MEDICAL IMAGE GENERATION SKIN CANCER CLASSIFICATION SKIN LESION CLASSIFICATION TRANSFER LEARNING
WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks
As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising.