Popular GitHub Repositories for Mediawiki Skin

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

BCDU-Net : Medical Image Segmentation

Highly Accurate and Efficient Burn detection and Classification trained with Deep Learning Model

[arXiv] The official code for "UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation".

[WACV 2024] Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation

HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation (WACV 2023)

A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.

Contextual Attention Network: Transformer Meets U-Net

(BIBM22) This is the official code repository for "MALUNet: A Muti-Attention and Light-weight UNet for Skin Lesion Segmentation".

An application that recommends personalised skincare and makeup products based on the skin metrics inferred from user's selfie using computer vision algorithms.

TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation

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

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

Official implementation code for Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation paper

A General-Purpose Multimodal Foundation Model for Dermatology

A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation

Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.

3クラス(肌、服、髪)のセマンティックセグメンテーションを実施するモデル(A model that performs semantic segmentation of 3 classes(skin, clothes, hair))

Pre-rendered regularization images of men and women, mainly faces, seeking to generate more realistic images (without wax skin)

【ACCVW2025 Oral】Offical Pytorch Code for "SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance"