A practical fast method for medical imaging transmission based on the DICOM protocol
Bibliographic record
Abstract
The standard format for medical imaging storage and transmission is Digital Imaging and Communications in Medicine (DICOM). Nowadays, and specifically with large amounts of medical images acquired by modern modalities, the need for fast data transmission between DICOM application entities is evident. In some applications, particularly those aiming to provide real-time services, this demand is critical. This paper introduces a method which provides a fast and simple way of image transmission by utilizing the DICOM protocol. The current implementations of DICOM protocol usually care more about connecting DICOM application entities. In the process of connecting two DICOM application entities, the format of the transmission (Transfer Syntax) is agreed upon. In this crucial step, the two entities choose an encoding that is supported by both and if one entity does not support compression the other one cannot use that option. In the proposed method, we use a pair of interfaces to deal with this issue and provide a fast method for medical data transmission between any two DICOM application entities. These interfaces use both compression and multi-threading techniques to transfer the images. The interfaces can be used without any change to the current DICOM application entities. In fact, the interfaces listen to the incoming messages from the DICOM application entities, intercept the messages, and carry out the data transmission. The experimental results show about 22% speed-up in Local Area Networks (LANs) and about 13-14 times speed-up in Wide Area Networks (WANs).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".