We lifted 8 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| riqianggao/gdp-hmm_aapmchallenge | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| GetIMRTAngleList | Ran | riqianggao/gdp-hmm_aapmchallenge/DICOM2NPZ.py code served (permissive licence) · get_code("cf7c30e6ecf1126e") |
| PlotGantry | Ran | riqianggao/gdp-hmm_aapmchallenge/toolkit.py code served (permissive licence) · get_code("d5518e57b45eb6f1") |
| convert_labels_back_to_BraTS | Ran | riqianggao/gdp-hmm_aapmchallenge/mednext/nnunet_mednext/dataset_conversion/Task032_BraTS_2018.py code served (permissive licence) · get_code("a2c8b38ba9393780") |
| cropped2ori | Ran | riqianggao/gdp-hmm_aapmchallenge/inference.py code served (permissive licence) · get_code("6d4f034c282d16be") |
| interpolate_line | Ran | riqianggao/gdp-hmm_aapmchallenge/toolkit.py code served (permissive licence) · get_code("ee2a1a5f095583f0") |
| interpolate_point_on_line | Ran | riqianggao/gdp-hmm_aapmchallenge/toolkit.py code served (permissive licence) · get_code("f309d2a1a999acd4") |
| offset_spatial_crop | Ran | riqianggao/gdp-hmm_aapmchallenge/inference.py code served (permissive licence) · get_code("a2df77bb085920ab") |
| GetVMATAngleList | Not yet run | riqianggao/gdp-hmm_aapmchallenge/DICOM2NPZ.py code served (permissive licence) · get_code("e0819cfef260f8b6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Varian Eclipse. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations is proposed. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. To our best knowledge, this dataset features more than 10 times number of plans compared to the largest existing well-curated public dataset. Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2501.11803")
get_code_for_paper("2501.11803")
have("2501.11803")
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