PyOrthanc: A Python Interface for Orthanc DICOM Servers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it