MedEvalKit: A Unified Medical Evaluation Framework

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An implementation of the paper Brain2Qwerty that translates brain EEG data into text for reading people's brains. There was no code so we made an implementation

MICCAI 2024: Welcome to the official repository of D-MASTER: Mask Annealed Transformer for Unsupervised Domain Adaptation in Breast Cancer Detection from Mammograms. This repository hosts the source code, pre-trained model weights, and benchmark dataset RSNA-BSD1K, supporting research in cross-domain breast cancer detection.

Skin Diseases Detection System

This deep learning model detects eye diseases using CNNs. Trained on an image dataset, it predicts conditions with high accuracy. Ideal for AI-driven medical diagnosis!

A deep learning model for classifying eye diseases using Convolutional Neural Networks (CNNs). Built with TensorFlow and Keras, trained on a medical eye dataset for accurate multi-class classification and analysis.

🔬 Aplikasi deteksi penyakit kulit menggunakan AI dan Computer Vision - Lomba Digiwar #1: Digital Application Challenge - VISIONARY: Unleashing Innovation through Computer Vision.

Production-ready RAG chatbot for heart disease information using FastAPI, PostgreSQL + pgvector (Neon), and React + Vite.

Deep learning model using VGG16 to classify chest X-rays into COVID-19, Pneumonia, TB, and Normal. Deployed via a local web app with real-time predictions.

This project applies machine learning to predict heart disease using clinical data. It covers data preprocessing, model building, and performance evaluation, aiming to support early diagnosis and healthcare decision-making through data-driven insights and AI-based prediction techniques.

An Agentic RAG system using LangGraph Designed to answer medical questions accurately and contextually

ai assisted emergency triage and hospital routing to reduce er overload and improve response time.

本仓库主要面向学生群体提供一些AI医疗的实践项目,AI框架主要为主要Pytorch,旨在通过代码入门医学AI(尤其是医学人工智能)领域,了解该领域的常用技术与软件包(pydicom.nibabel,monai,simpleITK等),希望能对你有所帮助

This repository presents an efficient approach for fine-tuning large language models for the medical domain using 4-bit quantization and LoRA techniques.

PyTorch-based pipeline includes data preprocessing, model inference, and performance evaluation with standard metrics (Dice score, Hausdorff distance). The repository provides tools for visualizing segmentation results and comparing MedSAM-2's performance against baseline models, offering insights into adapting foundation models for medical imaging

A Noise-Resilient Hybrid Imputation-Ensemble (NR-HIE) framework designed to bridge the generalizability gap in medical AI. Utilizing a triple-stream imputation strategy and stacked generalization, this model achieved 81.62% accuracy on external validation data, ensuring robust and medically safe diabetes prediction

Multi-class Lung Cancer Detection using GLCM Texture Features and Linear SVM with 95%+ Accuracy | Flask Web Application

Deep learning model for brain cancer detection using CNN architecture. Trained on multi-class MRI data with ImageDataGenerator and optimized with Adam

This project builds a deep-learning-based heartbeat sound classification system using MFCC features and multiple models including CNN, BiLSTM, and a Hybrid CNN–BiLSTM architecture. The system detects and classifies heart sounds into normal, murmur, and artifact categories, supporting early cardiac abnormality detection.

InsightMed turns complex medical records into simple, understandable health guidance using AI.

Predict COVID-19 from chest X-rays using a VGG16-based deep learning model. Fast, accurate, and deployed locally with a simple web interface

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Deep learning model for brain tumor classification using MRI images. Built with TensorFlow, Keras, and OpenCV, trained on CNN architecture, and optimized with Adam. 🎯🔥

This study audits and mitigates fairness issues in cardiac MRI segmentation across SIEMENS, Philips, and GE scanners. A baseline 2D U-Net showed spurious vendor bias, particularly for the minority GE domain. Implementing a Domain Adversarial Neural Network reduced F1-Score disparity, stabilizing recall and improving clinical safety.

🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using 🤖 machine learning to predict potential health risks for ❤️ heart, 🩸 kidney, 🏥 liver, and 🩹 diabetes conditions.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. AI in Smart Healthcare for Early Diagnosis leverages AI to analyze medical data and detect diseases early, enhancing patient care, enabling timely interventions, and improving overall healthcare outcomes.

SEHAT is an interactive Predictive Health Application using Streamlit and machine learning models to forecast diabetes and heart disease risks based on user health parameters. Enhance your health awareness with immediate insights and proactive management tools.

Lung Cancer Classification app with FastAPI and Docker

Prostate MRI Segmentation