Application of imaging to the laproscopic surgery
Bibliographic record
Abstract
One of the main components which facilitated the success of Minimally Invasive Surgery (MIS), e.g. laparoscopic surgery, is the utilization of a scope which can be fed through small incision hole. As a result, this offered the surgeon a two-dimensional view of the surgical site. In general, the (laparo)scope is held by the assistance surgeon which may not offer an steady images of the viewing area. Another main difficulty in performing laparoscopic surgery is the lack of three-dimension perception of the surgical site. This perception includes the lack visual information about the relative position of the surgical tools and the lack of volumetric perception of the abdominal cavity. This paper presents some preliminary results on application of image processing and imaging technique to the field of laparoscopic surgery. In particular the paper presents (a) models for the lens distortion which can be used for diagnostics purposes; (b) a method for tracking surgical tools; (c) sensing system for determining the size of the abdominal volume.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".