Segmentation of Polyps in Gastrointestinal Tract Images
Bibliographic record
Abstract
Detecting abnormal tissues can be overlooked during body screening procedures including endoscopy, bronchoscopy, and colonoscopy. Colonoscopy is a routine screening procedure that can examine inside of the large intestine. However, observants might not be able to detect anomalies at initial phase. Therefore, a precise method is needed to detect the abnormalities. In this paper, we have implemented three different convolutional neural networks to segment polyps in gastrointestinal tract images. First, UNet which consist of two parts contraction and expansion for segmenting medical images. In this model data augmentation is performed with elastic deformations to yield accurate results with very few annotated images.Then, we implemented TriUnet which consists of three UNet models. The last model DivergentNets is an ensemble of five segmentation models named as TriUnet, Unetplusplus, FPN, DeeplabV3 and DeeplabV3plus. We have also tested images by using color correction, image pyramid and specularity removal. Our results suggest that when we combine different segmentation models as DivergentNets, it produces better results than UNet and TriUnet.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".