MagnetOFuse: A Hybrid Tracking Algorithm for Wireless Capsule Endoscopy Within the GI Track
Bibliographic record
Abstract
Wireless Capsule Endoscopy (WCE) is a well-established diagnostic tool for visualizing the Gastrointestinal (GI) tract. Early diagnosis helps doctors to act beforehand and treat the disease on its earlier stages. Images taken by WCE do not provide location information. However, this data is useful for selecting proper treatment. In this paper, we have introduced a hybrid method called MagnetOFuse to localize the capsule inside the human GI track. The capsule is equipped with four side wall cameras and a magnet. Magnetic localization acquires the capsule’s global position using 9 three-axis Hall effect sensors. However, magnetic localization alone cannot distinguish between the capsule’s motion and the GI tract’s motion. To overcome this issue and increase the tracking accuracy, low-resolution mono-chromatic cameras are installed along the capsule’s side, which are responsible for measuring the capsule’s movement, not the involuntary motion of the GI system. A complete setup is designed to test the capsule and to perform the experiments. The results show that the proposed hybrid method has an average position error of only 3.5 mm and can compensate for the GI tract’s relative movements. Furthermore, environment parameters such as magnetic interference and the nonhomogeneous structure of the GI tract have little influence on our system compared to the available magnetic localization methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".