Computer aided detection of bleeding in capsule endoscopy images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The capsule endoscopy (CE) has been widely used to diagnose the diseases in human digestive tract because of its great breakthrough that it can view the entire small bowel without invasiveness. However, a tough problem associated with this new technology is that too many images to be examined by eyes cause a huge burden to physicians, so it is very useful to help the physician do diagnosis using computerized methods. In this paper, a new method aimed for bleeding region detection in CE images is proposed. This new approach mainly focuses on color texture feature, also a very important clue for the physicians to judge the status of the gastrointestinal tract. We propose a new idea of chromaticity moment as the color feature, which make full use of the Tchebichef polynomials and the illumination invariant of the HSI color space. Then, combined with the uniform local binary pattern (LBP), a traditional texture representation model, it can be used to discriminate normal regions and bleeding regions. Classification of bleeding regions with multilayer perceptron neural network is then deployed to verify the performance of the proposed new color texture features. Experimental results on our present bleeding image data sets show that this new scheme is promising in detecting bleeding regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it