Prostate cancer segmentation with multispectral MRI using cost-sensitive Conditional Random Fields
Bibliographic record
Abstract
Prostate cancer is a leading cause of cancer death for men in the United States. There is currently no widely adopted accurate noninvasive method for localizing prostate cancer using imaging. If such as technique were available it could be used to guide biopsy, radiotheraphy and surgery. However, current imaging techniques are limited due to inability to detect cancers, intensity changes related to non-malignant pathologies and interobserver variability. Recently, multispectral magnetic resonance imaging (MRI) has emerged as a promising noninvasive method for the localization of prostate cancer alternative to transrectal ultrasound (TRUS). This paper develops automated methods for prostate cancer localization with conditional random fields using multispectral MRI. We propose to combine cost-sensitive Support Vector Machines with Conditional Random Fields and show that this method results in higher accuracy of localization compared to other common methods. Our results also show that multispectral modality images helps to increase the accuracy of prostate cancer localization. Using multispectral MR images, we demonstrate the effectiveness of each algorithm by testing them on real data sets and compare them to recently proposed SVMstruct and Conditional Random Fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".