Incremental Instance Segmentation for the Gleason Tissues Driven Prostate Cancer Prognosis
Bibliographic record
Abstract
Prostate cancer (PCa) is the second most commonly diagnosed cancer in men and the fifth-highest cause of death globally. Early-stage prostate cancer is frequently asymptomatic and has an indolent course, requiring active observation. Early detection and recognition of Gleason tissue can help handle the PCa spread. Therefore, many deep learning-based systems have been proposed by researchers in order to screen the PCa. Moreover, acquiring such large-scale, well-annotated data can improve the performance of screening and detecting PCa. However, this process is typically challenging and impractical. This paper addresses this issue by proposing a novel knowledge distillation-driven instance segmentation framework. This approach is fused with incremental few-shot training and allows the traditional semantic segmentation models to grade the PCa utilizing instance-aware segmentation, along with the extraction of correlated samples of the Gleason tissue patterns. Furthermore, the proposed approach has been validated on a dataset that contains around 71.7M whole slide image patches. Our approach has outperformed the state-of-the-art models by 2.01% in terms of mean IoU and 9.69% in terms of F1 score for the extraction of Gleason tissue instances and grading PCa, respectively.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".