Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

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

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

Skin Lesion Classification Analysis: A Comparative Study

Deep learning pipeline for multi-type ISIC 2018 skin lesion classification with CNNs, preprocessing, augmentation, and training and inference support 🐙.

A deep learning pipeline for skin lesion classification using ISIC dataset with multiple deep learning cnn algorithms and advanced preprocessing including multithreaded loading, augmentation, and performance evaluation.

Автоматическое клиническое описание дерматоскопических изображений: признаки → бакетизация → ранжирование → Qwen2.5-7B

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

derm - dermatology/skin iq - intelligence, AI

Hybrid dermoscopy classifier: EfficientNet-B4 + SwinV2 + metadata fusion for 8-class ISIC 2019 skin lesion classification. Includes Grad-CAM, SLM explanations, StyleGAN2 synthetic augmentation, and a React clinical review UI

A computer vision and machine learning pipeline for automatic skin cancer detection from dermoscopic images. Achieves high accuracy using traditional ML techniques with preprocessing, feature extraction, and ensemble learning.

[MICCAI ISIC 2024] Code for "Lesion Elevation Prediction from Skin Images Improves Diagnosis"

Prompt framing bias in dermoscopy LLMs — JAAD

Reupload and updated code for Efficient and Effective Automated Digital Hair Removal from Dermoscopy Images by collective of authors from 2016. Code has been updated so it runs on windows and CUDA 13.x An example usage of how to use it with python added.

Projects in Data Science - Bsc in Data Science ITU CPH - Group Penguins

Pixel-level skin lesion segmentation using U-Net trained from scratch on HAM10000 — 0.9115 Dice score, 31M parameters, BCE + Dice loss, live demo on HuggingFace Spaces

Ablation study on extreme medical class imbalance — ResNet18 + DCGAN synthetic augmentation + Grad-CAM explainability on HAM10000 skin lesion dataset (70:1 imbalance ratio).

Carrier PCB for INVENSOM-6UL SOM — DermScope REVIVE handheld dermatoscope by Revive Medical Technology

EfficientNetB0 trained on HAM10000 — 74.15% accuracy across 7 skin disease classes, Grad-CAM explainability, class-weighted loss for imbalanced medical data, Google Colab T4 GPU

SynSkin Dataset