Prostate cancer localization with multispectral MRI based on Relevance Vector Machines
Bibliographic record
Abstract
Prostate cancer is one of the leading causes of cancer death for men. However, early detection before cancer spreads beyond the prostate can reduce the mortality. Therefore, in vivo imaging techniques play an important role to localize the prostate cancer for treatment. Although magnetic resonance imaging (MRI) has been proposed to localize prostate cancer, the studies on automated localization with multispectral MRI have been limited. In this study we propose combining the pharmacokinetic parameters derived from DCE MRI with T2 MRI and DWI. We also propose to use relevance vector machines (RVM) for automatic prostate cancer localization, compare its performance to support vector machines (SVM) and show that RVM can produce more accurate and more efficient segmentation results than SVM for automated prostate cancer localization with multispectral MRI.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".