A Graph-Based Multi-kernel Feature Weight Learning Framework for Detection and Grading of Prostate Lesions Using Multi-parametric MR Images
Bibliographic record
Abstract
Prostate cancer is the third leading causes of death in men. However, the disease is curable if diagnosed early. During the past decades, multi-parametric magnetic resonance imaging (mpMRI) has been shown to be superior to trans-rectal ultrasound (TRUS) in detecting and localizing prostate cancer lesions to guide prostate biopsies and radiation therapies. The goal of this paper is to develop a simple and accurate graph-based regression framework for voxel-wise detection and grading of prostate cancer using mpMRIs. In the framework, groups of features were first extracted from the mpMRIs, and a graph-based multi-kernel model was proposed to learn the weights of the groups of features and the similarity matrix simultaneously. A Lapalacian regression model was then used to estimate the PI-RADS score of each voxels which characterizes how likely a voxel is cancerous. Experimental results of detection and grading of prostate lesions evaluated by six metrics show that the proposed method yielded convincing results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".