Pathology Imagebase—a reference image database for standardization of pathology
Bibliographic record
Abstract
AIMS: Despite efforts to standardize histopathology practice through the development of guidelines, the interpretation of morphology is still hampered by subjectivity. We here describe Pathology Imagebase, a novel mechanism for establishing an international standard for the interpretation of pathology specimens. METHODS AND RESULTS: The International Society of Urological Pathology (ISUP) established a reference image database through the input of experts in the field. Three panels were formed, one each for prostate, urinary bladder and renal pathology, consisting of 24 international experts. Each of the panel members uploaded microphotographs of cases into a non-public database. The remaining 23 experts were asked to vote from a multiple-choice menu. Prior to and while voting, panel members were unable to access the results of voting by the other experts. When a consensus level of at least two-thirds or 16 votes was reached, cases were automatically transferred to the main database. Consensus was reached in a total of 287 cases across five projects on the grading of prostate, bladder and renal cancer and the classification of renal tumours and flat lesions of the bladder. The full database is available to all ISUP members at www.isupweb.org. Non-members may access a selected number of cases. CONCLUSIONS: It is anticipated that the database will assist pathologists in calibrating their grading, and will also promote consistency in the diagnosis of difficult cases.
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 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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.032 |
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".