Development of an Integrated System for Automatic Tumor Detection for PET-CT Images
Bibliographic record
Abstract
Accurate early cancer diagnosis helps guide optimal treatment.Hybrid imaging of 18 F-lablelledfluorodeoxyglucose (FDG) using hybrid positron emission tomography (PET) and x-ray computed tomography (CT) is highly sensitive for detecting and characterizing many cancer types with increasing clinical use.Having an effective and reliable tumor detection and segmentation system is a major challenge because of the wide variations in the clinical environments and tumors shapes and sizes.Therefore, an automated clinical system must address three requirements: (1) seamless integration with clinical systems, (2) reliable performance across varying datasets, and (3) handling of extreme conditions outside performance limits.We have developed an automated system composed of three main subsystems: (1) A system to classify CTs axially into anatomical regions, (2) multi-organ segmentation system, and (3) tumor detection/segmentation system.The anatomical classifier reduces the search space for organ segmentation, and the organ segmentation system reduces the search space for tumors.The subsystems manage variations in image representation such as intensity, imaging field of view, and reconstruction parameters.We have developed our own analytical solutions combined with
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".