BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases

Intelligent Python service with FastAPI for real-time heart disease predictions using machine learning. Features AI-assisted consultations, user authentication, analysis history, RESTful API, and comprehensive error handling. Secure and scalable solution for healthcare applications.

Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]

Official repository for the ACL 2025 Findings paper "Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA"

A Deep Learning Based approach for diagnosis of Schizophrenia using EEG brain recordings

A Vietnamese dataset of over 12 thousands questions about common disease symptoms. Perfect for researchers and developers building Vietnamese healthcare chatbots or disease prediction models.

This project leverages advanced AI agents from crewAI to assist doctors in diagnosing medical conditions and recommending treatment plans based on patient-reported symptoms and medical history. The solution uses Streamlit for the user interface and crewai library to define and manage AI agents and tasks.

Early detection of Autism Spectrum Disorder (ASD) is crucial for children's development, yet the diagnostic procedure remains challenging. EyeTism employs machine learning on eye tracking data from both high-functioning ASD and typically developing children (TD) to create a diagnostic tool based on their distinct visual attention patterns.

基于自然语言处理的智能医疗诊断系统

A MATLAB-based GUI tool that predicts diabetes risk using machine learning (Random Forest) on the Pima Indian dataset.

Machine learning diabetes risk predictor using KNN classifier. Built as part of Intel AI for Youth program with scikit-learn, pandas, and data visualization for early medical screening.

A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.

An AI-powered deep learning system using VGG16 transfer learning to classify brain tumors (glioma, meningioma, pituitary, no tumor) from MRI scans. Built with TensorFlow, deployed on Render with Flask.

Ethnic bias analysis in medical imaging AI: Demonstrating that explainable-by-design models achieve 80% bias reduction across 5 ethnic groups (50k images)

A full-stack SaaS platform for hosting and monetizing medical AI models. It features a complete credit-based payment system (Razorpay), JWT/OAuth2 authentication, a full admin dashboard, and an LLM-powered assistant. The platform is live with its first two models for clinical diagnostics.

🔥 让AI像专家一样诊断 | 3种创新方法 | 跨领域通用 | 精美可视化

Using TensorFlow Object Detection API to detect blood cells

Early detection of Autism Spectrum Disorder (ASD) is crucial for children's development, yet the diagnostic procedure remains challenging. EyeTism employs machine learning on eye tracking data from both high-functioning ASD and typically developing children (TD) to create a diagnostic tool based on their distinct visual attention patterns.

Built an end-to-end deep learning pipeline using ResNet-50 to classify retinal images into five stages of Diabetic Retinopathy. Applied transfer learning, image preprocessing, and AUC-based evaluation on the APTOS 2019 Kaggle dataset, achieving a 94% validation AUC—offering real-world potential in clinical diagnosis automation.