Kompression von Bilddaten: Aktueller Stand
Bibliographic record
Abstract
Trotz weitgehend übereinstimmender Ergebnisse der Empfehlungen zur Datenkompression in der Radiologie der Canadian Association of Radiologists (CAR), der Deutschen Röntgengesellschaft und des Royal College of Radiology verläuft die Einführung der Datenkompression durch die Industrie eher zögerlich. Der Vortrag stellt den aktuellen Stand der Diskussion zur Kompression Radiologischer Bilddaten dar. Die wesentlichen Ergebnisse der von der ESR durchgeführten Konferenz zum Thema Datenkompression werden dargestellt und zusammengefasst. Grundlage für die Darstellung ist das von der ESR verabschiedete Positionspapier zur Datenkompression in der Radiologie, welches unter Beteiligung namhafter internationaler Experten erarbeitet wurde. Lernziele: Update zur Datenkompression in der Radiologie Korrespondierender Autor: Kotter E Universitätsklinikum Freiburg, Abteilung Röntgendiagnostik, Hugstetterstr. 55, 79106 Freiburg E-Mail: elmar.kotter@uniklinik-freiburg.de
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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".