← all works
Deep semantic segmentation of natural and medical images: a review
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
Opus teacher head0.011
GPT teacher head0.307
- Teacher spread
- 0.295 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- PolyPublie (École Polytechnique de Montréal)
- Topic
- Radiomics and Machine Learning in Medical Imaging
- Field
- Medicine
- Canadian institutions
- Polytechnique MontréalUniversité de MontréalSimon Fraser University
- Funders
- —
- Keywords
- Computer scienceArtificial intelligenceSegmentationImage segmentationCategorizationContext (archaeology)Task (project management)Image (mathematics)Segmentation-based object categorizationDeep learningPattern recognition (psychology)Scale-space segmentationComputer visionGeography
- Has abstract in OpenAlex
- no