Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural networks with hybrid optimization algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is crucial to improve survival rates. This study proposes an automated classification framework using equivariant quantum convolutional neural networks (EQCNNs) optimized with a Hybrid Adam-Dingo and Quantum Artificial Hummingbird Algorithm (HybADOQAHA). Urinary biomarkers – creatinine, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), regenerating islet-derived protein 1 beta (REG1B), and trefoil factor 1(TFF1) were analyzed from 590 samples comprising healthy, benign, and PDAC cases. Pre-processing with dual-feature filtering and feature extraction via lifted Euler characteristic transform enhanced data quality. The experimental results demonstrate better accuracy, precision, recall, specificity, and Area Under the Curve (AUC) compared with baseline models, establishing the proposed method as a promising non-invasive diagnostic tool for early PDAC detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it