A python framework accelerating ML based discovery in the medical field by encouraging code reuse. Batteries included :)

Classification and Segmentation with Mask-RCNN of Skin Cancer using ISIC dataset

[MICCAI 2023] DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

International Skin Imaging Collaboration: Melanoma Project

The official command line tool for interacting with the ISIC Archive.

U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation

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

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

A template for submitting algorithms to the ISIC Challenge

ISIC Challenge submission platform.

A Python SDK Library for working with the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 4.

ISIC2016challenge

This project aims to apply Artificial Intelligence in the detection and classification of skin cancers.

This repository contains experiments using different XAI methods and ISIC2020 dataset.

This project aims to predict the presence of skin cancer using a hybrid deep learning model that integrates both tabular data and image data. The dataset used is the ISIC dataset, which contains skin lesion images along with associated metadata. The model achieves an accuracy of 88% on the test set.

ISIC Archive API v2 download images by ISIC ID

This project combines traditional machine learning approaches with advanced deep learning techniques to assist healthcare professionals in early diagnosis and improve patient outcomes.

Repository for the paper: "Domain Adaptation for Skin Lesion: Evaluating Real-World Generalisation"

Source code for the paper: "Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?"

Parses the "Classification of Economic Activities" (wz2008) issued by the Statistisches Bundesamt to build multiple hierarchically structured trees.