Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Authors: Riqiang Gao, Florin-Cristian Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. |
| Researcher Affiliation | Industry | 1Digital Technology and Innovation, Siemens Healthineers, Princeton NJ, USA 2Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany 3Varian Medical Systems, Siemens Healthineers, Helsinki, Finland. |
| Pseudocode | Yes | Algorithm 1 RLS training |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code of the described methodology. |
| Open Datasets | Yes | The HN site includes three datasets: 1) the HNd set contains 493 patients after quality assurance used for training and test with a train/validation/test split, 2) two external test sites from TCIA: HNe1 (Bejarano et al., 2018) with 31 patients and HNe2 (Grossberg et al., 2020) with 140 patients after filtering. The Pros site is from a public dataset with access permission requirements, including 555 patients after filtering. |
| Dataset Splits | Yes | HNd set contains 493 patients after quality assurance used for training and test with a train/validation/test split, 2) two external test sites from TCIA: HNe1 (Bejarano et al., 2018) with 31 patients and HNe2 (Grossberg et al., 2020) with 140 patients after filtering. The Pros site is from a public dataset with access permission requirements, including 555 patients after filtering. |
| Hardware Specification | Yes | GPU type: NVIDIA RTX A4500 |
| Software Dependencies | Yes | deep learning platform: Py Torch 1.13 |
| Experiment Setup | Yes | RL iterations: 20000 Update epochs: 2 batch_size: 96... discount factor gamma: 0.99... learning rate: 1e-4... optimizer: Adam W |