Integrated system for automatic detection of representative video frames in wireless capsule endoscopy using adaptive sliding window singular value decomposition
Bibliographic record
Abstract
Wireless capsule endoscopy (WCE) is a non‐invasive diagnosis method that allows recording a video as the capsule travels through the gastrointestinal (GI) tract. The practical drawback is producing a long clinical video in which the review process by an experienced specialist is tedious. Automated summarisation methods can reduce the evaluation time by experts as well as errors in manual interpretation. The proposed approach consists of three main steps as follows: First, an adaptive sliding window singular value decomposition is employed to extract representative video frames. Then, adaptive contrast diffusion is utilised to increase the visibility of WCE frames. At the end stage, a novel knowledge‐based method is developed to segment video frames into four topographic zones of GI tract, which are oesophagus, stomach, small intestine and large intestine. The authors have evaluated the proposed framework in the presence of 30 local datasets as well as publicly available KID database. The average recall and precision were estimated by 0.86 and 0.83, and by 0.82 and 0.83 for KID database, respectively. Their results reveal that significant reduction in the review time is feasible using the proposed technique. Quantitative results of summarisation show that the proposed method is more effective than three methods in the literature.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".