Skin Lesion Classification
36 papers with code • 1 benchmarks • 7 datasets
Most implemented papers
Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge
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
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
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
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
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
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.