Healthcare Sector Challenges

Current challenges that MediNet solves

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Data Privacy

Institutions control access to sensitive data and approval for collaborative research.

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Limited Data

Each hospital trains models with their own limited data, resulting in poorly representative models.

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Lack of Collaboration

There is no secure way to collaborate on medical AI projects between institutions.

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Biased Models

Models trained with data from a single institution may have significant population biases.

The Solution: Federated Learning

MediNet uses federated learning to enable multiple hospitals to train a joint model while keeping training datasets at their institutions.

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Data Never Leaves the Hospital

Only model updates are shared, never patient data

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More Accurate Models

Evaluate the effect of data diversity in your own study

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Institutional Control

Review dataset policy, deployment controls and applicable study requirements

Diagrama de Aprendizaje Federado

Federated learning flow: each hospital trains locally, only model updates are shared

Advanced Technology

Robust and reliable technology stack

Frontend

Django Templates Bootstrap 5 JavaScript ES6+ Chart.js

Backend

Django 4.x REST API SQLite Django ORM

IA & ML

Flower (flwr) PyTorch Scikit-learn NumPy/Pandas

Deployment

Docker Gunicorn Django Server

Key Features

Everything you need for medical federated learning

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Interactive Dashboard

Real-time monitoring of training progress with advanced metrics

Dashboard preview
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Visual Model Designer

Create neural network architectures with drag-and-drop interface

Model designer preview
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Federated Connections

Secure management of connections between hospitals with automatic validation

Connections preview

Ready to Start with MediNet?

Join secure and effective medical collaboration

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