U-KAN: Hybrid Spatial-Functional Deep Learning for Tumor Depth Estimation in Fluorescence-Guided Cancer Surgery
Bibliographic record
Abstract
Accurately estimating subsurface tumor depth during fluorescence-guided surgery remains an open challenge, as current intraoperative methods provide only surface-level fluorescence contrast without quantitative depth information. Spatial Frequency Domain Imaging offers a pathway toward quantitative fluorescence imaging by capturing both reflectance and optical property maps; however, its application to tumor depth estimation is limited by the scarcity of patient-derived datasets and significant domain gaps between simulated and experimental measurements. To address these challenges, we propose U-KAN, a hybrid deep learning framework that combines a siamese attention U-Net for spatial feature extraction with a Kolmogorov-Arnold Network (KAN) regression head for functional depth mapping. The U-Net captures morphological and structural cues from fluorescence and optical property inputs, while the KAN performs nonlinear pixelwise regression to improve generalization under limited data. Experiments on diffusion-theory, Monte Carlo, and patient-derived phantom datasets demonstrate that the hybrid model achieves more accurate and robust tumor depth estimation than either model alone, establishing a promising foundation for quantitative, depthaware fluorescence imaging in surgical oncology. U-KAN reduced tumor-region MAE by more than 30 % and cut minimum-depth errors nearly in half on phantom data, demonstrating markedly improved cross-domain robustness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".