PitVis-2023 Challenge: Endoscopic Pituitary Surgery videos
Bibliographic record
Abstract
The first public dataset containing both step and instrument annotations of the endoscopic TransSphenoidal Approach (eTSA). The dataset includes 25-videos (video_{video_number}.mp4) and the corresponding step and instrument annotation (annotations_{video_number}.csv). Annotation metadata mapping the numerical value to its formal description is provided (map_steps.csv and map_instrument.csv), as well as video medadata (video_encoder_details.txt). Helpful scripts and baseline models can be found on: https://github.com/dreets/pitvis. This dataset is released as part of the PitVis Challenge, a sub-challenge of the EndoVis Challenge hosted at the annual MICCAI conference (Vancouver, Canada on 06-Oct-2024). More details about the challenge can be found on the challenge website: https://www.synapse.org/Synapse:syn51232283/wiki/621581. The companion paper with comparative models is titled: "PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery" (Adrito Das et al.). Please cite this paper if you have used this dataset: https://doi.org/10.1016/j.media.2025.103716.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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; both teacher heads agree on what is shown here.
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".