medical-5

Skin Lesion Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in skin-lesion-classification-5

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Dataset

Best Model

Actions

ISIC 2019

Libraries

i

Use these libraries to find skin-lesion-classification-5 models and implementations

Datasets

MSK

ISIC 2020 Challenge Dataset

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Most implemented papers

FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

Jing Zhang, Dacheng Tao, Benteng Ma, Shanshan Zhao, Yong Xia•Wed Dec 01 2021

A novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks, and preserves the semantics of the poisoned image pixels, and can perform attacks on both classification and dense prediction models.

104

Content

MCSI

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Paper Graph

Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge

Md Ashraful Alam Milton•Tue Jan 29 2019

This study experimented with various neural networks which employ recent deep learning based models like PNASNet-5-Large, InceptionResNetV2, SENet154, InceptionsV4, and InceptionV4 to detect melanoma and skin lesion cancers.

148 0

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Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification

Amir Atapour-Abarghouei, Peter J. Bevan•Sat Feb 05 2022

An efficient yet effective algorithm for automatically labelling the skin tone of lesion images is proposed, and this is used to annotate the benchmark ISIC dataset and to use these automated labels as the target for two leading bias unlearning techniques towards mitigating skin tone bias.

38 0

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WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks

Y. Lee, Sang-Hyuk Jung, H. Won•Thu Aug 09 2018

The WonDerM pipeline is designed, that resamples the preprocessed skin lesion images, builds neural network architecture fine-tuned with segmentation task data, and uses an ensemble method to classify the seven skin diseases.

41 0

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Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

S. Avila, Fábio Perez, Eduardo Valle•Sun Apr 28 2019

The aim in this paper was not to maximize performance, but it was found that it was easy to reach AUCs comparable to the first place on the ISIC Challenge 2017, and found that random choice was also competitive.

60 0

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Data Augmentation for Skin Lesion Analysis

S. Avila, Fábio Perez, Eduardo Valle, C. Vasconcelos•Tue Sep 04 2018

The results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images.

183 0

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Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting

A. Schlaefer, N. Gessert, R. Werner, Thilo Sentker, F. Madesta, Rüdiger Schmitz, Helge Kniep, I. Baltruschat•Mon May 06 2019

The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance, and pretrained architectures can be readily used with high-resolution images without downsampling.

149 0

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An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis

Q. Dou, P. Heng, Hao Chen, J. Qin, Xueying Shi, Cheng Xue•Wed Sep 04 2019

This paper presents a novel active learning framework for cost-effective skin lesion analysis, to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance.

42 0

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Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data

A. Schlaefer, N. Gessert, M. Nielsen, M. Shaikh, R. Werner•Tue Oct 08 2019

This paper addresses skin lesion classification with an ensemble of deep learning models including EfficientNets, SENet, and ResNeXt WSL, selected by a search strategy and predicts an additional, unknown class with a data-driven approach and makes use of patient meta data with an additional input branch.

295 0

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Melanoma detection using adversarial training and deep transfer learning

H. Zunair, Benourida Hamza•Sun Apr 05 2020

Interestingly, it is shown that the proposed two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning toward melanoma detection leads to context based lesion assessment that can reach an expert dermatologist level.

102 0

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