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
Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge
miltonbd/ISIC_2018_classification
•
•
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).
Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions
This report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection.
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
pbevan1/detecting-melanoma-fairly
•
•
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.
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
jhl-det/cva-net
•
•
Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
Abdulrahman-Adel/Skin-Cancer-Detection
•
•
This short paper reports the method and the evaluation results of Casio and Shinshu University joint team for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part 3: Lesion Classification hosted by ISIC.
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.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
YtongXie/MB-DCNN
•
•
Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
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.
Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation
vribeiro1/skin-lesion-segmentation-agreement
•
•
We also evaluate how conditioning the ground truths using different (but very simple) algorithms may help to enhance agreement and may be appropriate for some use cases.
