RiqiangGao/GDP-HMM_AAPMChallenge
The code base for Generalizable Dose Prediction for Heterogenous Multi-Cohort and Multi-Site Radiotherapy Planning (GDP-HMM) challenge at AAPM 2025
This project provides tools and tutorials for developing models to predict radiation dose distributions for cancer treatment. It helps clinical physicists and researchers in radiotherapy planning by taking patient CT scans, tumor outlines (PTVs/OARs), and beam geometry as input. The output is a 3D radiation dose distribution, similar to what's generated by treatment planning systems, enabling the development of more generalizable and accurate dose prediction algorithms.
Use this if you are a clinical physicist, medical physicist, or researcher working in radiation oncology and want to develop or evaluate AI models for predicting radiation dose during treatment planning.
Not ideal if you are looking for a ready-to-use clinical tool for immediate patient treatment planning, as this is a development framework for a research challenge.
Stars
66
Forks
16
Language
Python
License
MIT
Category
Last pushed
Dec 05, 2025
Commits (30d)
0
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