PyOrthanc: A Python Interface for Orthanc DICOM Servers
Bibliographic record
Abstract
PyOrthanc is an open-source Python library that provides a comprehensive interface for interacting with Orthanc (Jodogne, 2018), a lightweight, versatile, open-source DICOM server for medical imaging in healthcare and research environments. Statement of needDigital Imaging and Communications in Medicine (DICOM) (Committee, 2020) is the standard for managing and transmitting medical images.Orthanc has gained popularity for its lightweight nature and versatility.However, programmatically interacting with Orthanc servers from its REST API can be complex, especially for those unfamiliar with RESTful APIs.PyOrthanc addresses this challenge by providing a client-side, Pythonic interface to Orthanc servers, abstracting away the complexities of HTTP requests and DICOM data handling.This is in contrast to the Orthanc Python plugin, which offers a powerful means to extend Orthanc's functionality directly within the server environment. Features and FunctionalitiesPyOrthanc offers a wide range of features that facilitate data manipulation with Orthanc servers:
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.059 |
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".