Lesion Classification
49 papers with code • 2 benchmarks • 7 datasets
HAM10000
ISIC 2018 Task 1
ISIC 2017 Task 1
MSK
BCN_20000
Retinal-Lesions
ISIC 2017 Task 2
Most implemented papers
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
xmichelleshihx/AL_Skin_SAAS
•
•
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.
Learn to Segment Retinal Lesions and Beyond
This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
hasibzunair/adversarial-lesions
•
•
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.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
hasibzunair/adversarial-lesions
•
•
Journal of Physics in Medicine and Biology 2020
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.
Debiasing Skin Lesion Datasets and Models? Not So Fast
Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data.
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation
vribeiro1/skin-lesion-segmentation-agreement
•
•
Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data.
A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
King-HAW/DC-MT
•
•
With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.
Method to Classify Skin Lesions using Dermoscopic images
In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model.
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approach
Abhishek-st/Skin-Lesion-Analysis
•
•
International Journal of Scientific and Research Publication 2021
Hence, the objective of this research paper is to develop an intelligent system to detect skin cancer at the earliest stage possible by a skin lesion classifier.
