BIM: an open ontology for the annotation of biomedical images.
Bibliographic record
Abstract
Biomedical images published within the scientific literature play a central role in reporting and facilitating life science discoveries. Existing ontologies and vocabularies describing biomedical imag-‐ es, particularly sequence images, do not provide sufficient seman-‐ tic representation for image annotations generated automatically and/or semi-‐automatically. We present an open ontology for the annotation of biomedical images (BIM) scripted in OWL/RDF. The BIM ontology provides semantic vocabularies to describe the manually curated image annotations as well as annotations gener-‐ ated by online bioinformatics services using content extracted from images by the Semantic Enrichment of Biomedical Images (SEBI) system. The BIM ontology is represented in three parts; (i) image vocabularies -‐ which holds vocabularies for the annotation of an image and/or region of interests (ROI) inside an image, as well as vocabularies to represent the pre and post processing states of an image, (ii) text entities -‐ covers annotations from the text that are associated with an image (e.g. image captions) and provides semantic representation for NLP algorithm outputs, (iii) a provenance model -‐ that contributes towards the maintenance of annotation versioning. To illustrate the BIM ontology’s utility, we provide three annotation cases generated by BIM in conjunction with the SEBI image annotation engine.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".