Desenvolvimento de software para processamento de imagens quantitativas em ressonância magnética
Bibliographic record
Abstract
The use of quantitative analysis in medical radiology has been of great value in the detection of not accessible alterations in the simple visual analysis, said qualitative, for being very subtle, or for not being present in conventional magnetic resonance image techniques.However, certain types of quantification demand the acquisition of high cost softwares and computational platforms, beyond specialized workmanship, with technical knowledge in computation, to operate in non intuitive environments.In this scenery the objective of this work was the implementation of a software for analysis of transference of magneti zation in nuclear magnetic resonance images that works in IBM-PC platform and free operational systems as GNU/Linux.So, an algorithm for reading of standard DICOM 3.0 codified images was elaborated, an algorithm for the construction of Magnetization Transfer Ratio maps of acquired volume, and a visualizer with friendly interface for segmentation and analysis of the results.Finally the software made the opening of DICOM image possible.It also generated in efficient way the maps of percentage difference among the images without and with the pulse of magnetization transfer (MT), also making devices of movement corrections possible, when they are not very intense.It allowed the delineation of regions of irregular interest, with good visibility of the results.As standard control, the results were compared with the set of tools of the McGill
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".