TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

中文医疗对话模型扁鹊(BianQue)

BCDU-Net : Medical Image Segmentation

[ACL 2024 Findings] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning https://arxiv.org/abs/2311.10537

DICOM Viewer

Functioning Doctor/Healthcare Catalog App created using Dart with Flutter. Stores and loads data from Firebase Firestore DB.

Medic.ly - Health and Doctor Appointment App made with Flutter. With this you can book an online appointment to a doctor in advance.

This repository is about the OwnHealthRecord Application Web App

Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models

A novel medical large language model family with 13/70B parameters, which have SOTA performances on various medical tasks

To cure symptoms of various disease using medicines at home this app will act as guideline. Few animations are used and Firebase is used as database.

[ICLR'26] MedAgentGYM: Training LLM Agents for Code-Based Medical Reasoning at Scale

Exploring the potential of fine-tuning Large Language Models (LLMs) like Llama2 and StableLM for medical entity extraction. This project focuses on adapting these models using PEFT, Adapter V2, and LoRA techniques to efficiently and accurately extract drug names and adverse side-effects from pharmaceutical texts

Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method

Surgical Image Guidance and Healthcare Toolkit

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

An Open Source Hospital Management System

The Labinator Medical Symptom Checker helps you understand the possible causes of symptoms in children and adults. It uses the USWDS UI components and programmed in JavaScript.

Awesome list for deep learning on medical research

本项目利用医学领域的 CoT 数据对 Deepseek-R1-Distill-Qwen-7B 进行微调，通过 QLoRA 量化和 Unsloth 加速训练，显著提升模型在复杂医学推理任务中的慢思考能力。知识蒸馏技术使轻量级模型获得大模型的推理优势，实现高效、准确且具有解释性的医学问答系统。