dermoscopy

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

Prompt-driven diffusion for jointly generating dermoscopic images and lesion masks, with downstream classification and segmentation evaluation.

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

Skin Lesion Classification Analysis: A Comparative Study

CNN ensemble for skin lesion classification — BACC 0.846 ± 0.009 · Clinical threshold calibration · ResNet-50 + DenseNet-121 + EfficientNet-B3 · ISIC 2018 Task 3

Streamlit Application live to use for free forever.

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

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.

Melanoma classification service and training notebooks behind the MoleCare skin-health apps. Research and education only — not a medical device.

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

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

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

Pixel-level skin lesion segmentation using a custom U-Net deep learning model trained on ISIC 2016 dermoscopic images.

derm - dermatology/skin iq - intelligence, AI

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

TÜBİTAK 2209-A: YOLO + ConvNeXt + Swin ensemble ile mobil cilt kanseri tarama sistemi (mel recall 0.87, cihaz üzerinde ONNX inference)

Melanoma detection on dermoscopy images with ten classical and deep methods under a leakage-audited protocol. TOK 2026 paper.