Prostate biomedical images segmentation and classification by using U-NET CNN model
Bibliographic record
Abstract
Prostate cancer is one of the most widespread types of cancer among men. The successful treatment of prostate cancer is based on accurate diagnosis. Gleason grading patterns system is one of the most efficient methods in diagnosing histological biopsies of prostate images by pathologists. Automatic detection and segmentation of the prostate on histological Gleason grading system is still the most powerful prognostic tool. In this paper, we propose a powerful deep convolutional neural network (CNN) technique called U-Net module to predict the prostate Gleason score based on tissue microarray (TMA) images. We developed a U-Net model for object semantic segmentation, where the goal is to precisely label each pixel in an image as being part of a given object (foreground) or not (background). Our proposed U-Net model of prostate segmentation achieved a mean test accuracy of 96%. The model achieved a mean Dice index coefficient (DI) of 0.56 and a mean IOU of 0.95 that show how close the output segments are to the corresponding lesions in the ground truth maps.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".