Skin Lesion Classification
41 papers with code • 2 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Skin Lesion Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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HAM10000
MSK
ISIC 2019
BCN_20000
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.
Deep Multimodal Guidance for Medical Image Classification
Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference.
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning
Deep learning models have achieved great success in automating skin lesion diagnosis.
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.
