AI-KODA Dataset: An AI-Image Dataset for Automatic Assessment of Cleanliness in Video Capsule Endoscopy as per Korea-Canada Scores
Bibliographic record
Abstract
AI-Image Dataset for Automatic Assessment of Cleanliness in Video Capsule Endoscopy as per KODA scoresArtificial Intelligence-Korea Canada (AI-KODA) dataset is a medically annotated, multi-label image AI dataset collected from Department of Gastroenterology and HNU, All India Institute of Medical Sciences, New Delhi. It consists of 2173 video capsule endoscopy frames with KODA score labels.There are three folders in the dataset namely:ImagesLabelsSample python files with no augmentationThe Images folder contains 2173 Video Capsule Endoscopy frames of 28 patients. 1539 frames are sequential in nature and were obtained after every 5 minutes from the 28 patient videos. 634 frames are non-sequential in nature and were obtained randomly from the 28 patient videos. F minutes in the image path represents five-minute frames. DF in the image path represents default frames. The images are of 320 x 320 resolution.The labels folder contains two types of files in excel and CSV format. One is multi-hot encoded in zeros and ones format. Another one contains the exact labels. Sample python files are attached for its use. Please note that no augmentation has been performed. Users may augment themselves as per case use. Further information will be shared upon acceptance of the manuscript(s) in consideration.Authors are thankful to Dr. Mohammad Tabish, Dr. Rajat Bansal and Dr. Syed Ahmed from Department of Gastroenterology & HNU, All India Institute of Medical Sciences, New Delhi for helping in annotation of the dataset. Thanks to Nikita Garg for helping in the development of the AI-KODA application. The authors also acknowledge the support of SERB, a statutory body of the Department of Science and Technology, Government of India for funding this research work under the Core Research Grant (CRG/2022/001755).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".