Skin Lesion Classification
36 papers with code • 1 benchmarks • 7 datasets
HAM10000
MSK
BCN_20000
SD-198
Most implemented papers
Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge
miltonbd/ISIC_2018_classification
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In this paper, we studied extensively on different deep learning based methods to detect melanoma and skin lesion cancers.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
pbevan1/detecting-melanoma-fairly
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Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment.
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.
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).
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
learningtitans/cvpr-skin-solo-ensemble
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We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models.
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.
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
xmichelleshihx/AL_Skin_SAAS
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In this paper, we present a novel active learning framework for cost-effective skin lesion analysis.
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.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
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.
