Instructions for the removal of duplicate image files from within individual ISIC datasets and across all ISIC datasets.

ISIC 2019 - Skin Lesion Analysis Towards Melanoma Detection

Skin Lesion Analysis Towards Melanoma Detection

The souce code of MICCAI'23 paper: Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation

Source code and experiments for the paper: "Dark Corner on Skin Lesion Image Dataset: Does it matter?"

ISIC2019 skin lesion classification (binary & multi-class) as well as segmentation pipelines using VGG16_BN and visual attention blocks. The project features improving the results found in the literature by implementing an ensemble architecture. This project was developed for "Computer Aided Diagnosis - CAD" course for MAIA masters program.

Analysis of the dermoscopic image processing pipeline toward optimally segmenting skin lesion regions and classifying lesion types using adversarial and generative deep learning.

Developing a CNN-based model to diagnose skin cancer using the ISIC-2019 dataset.

The aim of this study is to develop a deep learning model using CNNs for accurate skin cancer diagnosis from the ISIC-2019 dataset and to optimize hyperparameters using differential evolution algorithms.