Adaptation of an internet‐based DICOM viewer for use with the Medical Imaging Resource Center – Teaching File System (MIRC‐TFS)
Bibliographic record
Abstract
DICOM is the standard file format for medical imaging; however, storage, transmission and viewing of these images over the internet for teaching and learning purposes outside of the clinical setting presents a challenge as they do not lend themselves well to rapid display and interrogation due to their atypical file format. The aim of this project was to utilize the web technology standard HTML 5 offers to deploy a DICOM viewer that functions well, directly in the user's browser, without the need for complex client side‐applications, application switching, or user login requirements. To achieve this we have adapted the open source HTML 5 DICOM viewer, Oviyam, for integration with the Radiological Society of North America's Medical Imaging Resource Center – Teaching File System (MIRC‐TFS) on a virtual server to facilitate access to DICOM objects for teaching and learning. For learners a plugin was developed to transform the DICOM images in MIRC‐TFS into JPEG format resulting in a reduction in file size allowing users to view the images more rapidly while supporting higher user traffic for viewing images concurrently, as frequently occurs with large class sizes. In addition, a second plugin was developed for BlackBoard Learn that allows instructors to browse the MIRC‐TFS teaching cases and insert them alongside their other course materials. The tool set and image viewing capabilities of Oviyam were enhanced to include annotation tools allowing students to create marked up versions of the images for evaluation of their knowledge. The deployment of this solution has significantly facilitated the interaction of teachers and learners with DICOM based images for use in their academic environment.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".