Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning
Why this work is in the frame
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Bibliographic record
Abstract
Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17[Formula: see text] 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it