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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".