Utilizing Multi-Agent Reinforcement Learning with Encoder-Decoder Architecture Agents to Identify Optimal Resection Location in Glioblastoma Multiforme Patients

This project develops an AI system offering an end-to-end solution for aiding doctors with diagnosis and treatment planning for Glioblastoma Multiforme (GBM), the deadliest human cancer. It uses multi-agent reinforcement learning to identify optimal resection locations, aiming to improve the currently low 5-year survival rate of 5.1%.