Mining Brain Tumors and Tracking their Growth Rates
Bibliographic record
Abstract
Mining brain tumors and tracking their growth trends in the course of magnetic resonance imaging is an important task that assists medical professionals to describe the appropriate treatment. Nevertheless, applying conventional techniques to carry out this process manually is time-consuming and often unreliable and insufficiently accurate. Automating this process is a challenging task due to the fact of the fractal shape of tumor and its biological structure, which is often, has a high degree of intensity and textural similarity between normal areas and tumor tissues. Moreover, tumor uptake measurements are not easy given the small size of many tumors, the limitations of spatial resolution, and the change of tumor location from slice to slice across the brain. Furthermore, the arbitrary shape of tumors makes it extremely hard, if not impossible, to adopt traditional geometric rules for tumor measurements. In this paper, we present a computational approach for modeling and mining a large number of MRI data for patients with brain tumors. In this approach, we adopt a spatial data mining technique to extract useful information from MRI data in order to identify the size of tumors and growth trend, as well as classifying tumors of patients upon specific similarity measures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".