GlandNet: Automated Gland Segmentation for Colorectal Cancer Diagnosis using UNet
Bibliographic record
Abstract
Histopathological image study is vital for diagnosing diseases like cancer by the correct identification and analysis of cellular structures such as glands. Nevertheless, segmentation of these structures in particular for distorted or densely packed glands remains a huge challenge for most automation methods. Even though deep learning based algorithms look promising, they continue struggling with segmented gland morphologies, while traditional methods present challenges in adequately separating tightly clustered glands. This paper proposes GlandNet, a UNet based architecture that provides instance segmentation for better gland segmentation. By undertaking these tasks in conjunction, this approach enhances generalization, especially in difficult cases. In this work, GlandNet is evaluated using different metrices, establishing new benchmarks of state-of-the-art scores: 95%, 95%, and 71.35 pixels, respectively. Further comprehensive experiments on the benchmark GlaS dataset demonstrate that, particularly in delicately separating densely clustered glands, GlandNet indeed outperforms the existing state of affairs. This development increases the performance and efficiency of histopathological slide analysis, leading to better disease diagnostics and medical research.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".