Rank3 Code for ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, Task 3

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

TensorFlow implementation of a comprehensive comparison of various SSL (Semi-Supervised Learning) approaches in image segmentation, featuring our novel Inconsistency Masks (IM) method.

This repository contains the code for semantic segmentation of the skin lesions on the ISIC-2018 dataset using TensorFlow 2.0.

[MICCAI 2023] Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

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

Skin lesion image analysis that draws on meta-learning to improve performance in the low data and imbalanced data regimes.

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

Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.

Robust learning on ISIC 2018, based on Learning with Noisy Labels via Sparse Regularization (ICCV 2021).

A comparative study for skin lesion segmentation and melanoma detection where deep learning methods can perform very well without complex pre-processing techniques except for normalization and augmentation.