Efficient Platform as a Service (PaaS) Model on Public Cloud for CBIR System
Bibliographic record
Abstract
Medical image processing requires handling a huge amount of data.Unstructured big data can create some issues related to latency.Distributed architectures based on parallelism can alleviate a latency problem.Also, achieving image availability in case of a server failure in such a huge system increases the latency time.To solve the challenges of latency in image processing in an enormous system, we propose a new platform for information retrieval in databases consisting of digital imaging communication in medicine (DICOM) files.The platform is based on a Decision Tree.The servers in this platform are distributed and work in a parallel way.Also, a fault tolerant system based on time triggered protocol is proposed to ensure image availability and minimize image recovery latency in the case of a server failure.The main goal of this proposal is to select images from DICOM files similar to an image proposed in a query, using the principle of content based image retrieval (CBIR).Also, this platform helps radiologists with the diagnosis of medical images.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".