Source code for the paper 'Data Augmentation for Skin Lesion Analysis' — 🏆 Best Paper Award at the ISIC Skin Image Analysis Workshop @ MICCAI 2018

Implementation for MICCAI DART paper: 'Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification'

Experiments of the DAI in Healthcare project - skin lesions images use case - using Flower

Implementation for ICML 2022 paper: 'Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification'

This repository deals with generating 'malign' synthetic samples from 'benign' samples using CycleGAN to mitigate class imbalance and detecting Melanoma using a new balanced skin lesion image dataset.

This is a final-year project backend primarily focused on image classification for melanoma skin cancer. I have implemented a Convolutional Neural Network (CNN) AI model for this purpose.

Classification of malignt or benignt melanoma using the ISIC 2020 Challenge Dataset.

Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".

Ai powered web app that can analyze a picture of a skin lesion and instantly classify it into one of 7 types - including cancerous lesions like melanoma.

Melanoma classification using computer vision techniques on SIM-ISIC 2020 dataset

Melanoma Detection Tool : Website

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

Project of "Laboratorio di Intelligenza Artificiale" on machine learning: nevi classification with neural networks for automatic diagnosis of melanoma.

Melanoma Skin Cancer Diagnosis based on Dermoscopic Features and DNA Mutations

This project focuses on the VisioMel Challenge whose goal is predicting melanoma relapse. Recent advancements in SSL and WSL offer promising new solutions for improving the accuracy of cancer relapse detection.

Melanoma Detection Tool : REST APIs

This repository uses Deep Learning methods to develop a neural network that can effectively detect Melanoma.