Modularized Adaptive Processing Neural Network (MAP-NN) for Low-dose CT with Radiologists-in-the-loop | Available Intellectual Property | Rensselaer Polytechnic Institute

Modularized Adaptive Processing Neural Network (MAP-NN) for Low-dose CT with Radiologists-in-the-loop

RPI ID: 2019-023-201

Innovation Summary:
This invention introduces a modularized adaptive processing neural network architecture designed for efficient and scalable AI applications. The system dynamically adjusts its structure and processing pathways based on input complexity and task requirements. It supports real-time learning and inference, making it ideal for edge computing and resource-constrained environments. The modular design allows for easy customization and integration into diverse systems.

Challenges / Opportunities:
Traditional neural networks are often rigid and computationally intensive, limiting their use in dynamic or low-power environments. This invention addresses these limitations by enabling adaptive processing that balances performance and efficiency. It opens opportunities for deploying AI in mobile, embedded, and real-time systems.

Key Benefits / Advantages:
✔ Adaptive architecture adjusts processing based on task complexity
✔ Modular design facilitates customization and scalability
✔ Efficient computation reduces power and resource consumption
✔ Real-time capability supports fast learning and inference

Applications:
• Edge AI and IoT devices
• Autonomous systems and robotics
• Smart sensors and embedded AI
• Real-time analytics and control systems

Keywords:
#Adaptiveneuralnetwork #modularAI #edgecomputing #realtimeinference #scalablearchitecture

Intellectual Property:
US Issued Patent 11,682,110 B2