Spatial Imputation : An Approach for Missing Raman Spectroscopy Prostate Cancer Data
Bibliographic record
Abstract
According to the 2023 Canadian Cancer Statistics, 1 in 4 Canadians will die from cancer. For males, prostate cancer accounts for 1 in 5 new diagnoses. One of the ways to reduce cancer mortality is through early diagnosis. Medical physicists have been developing different diagnostic methods for cancer detection and radiation treatment analysis including using Raman spectroscopy. However, one of the main limitations of Raman spectroscopy is that the data is very prone to saturation and cosmic rays making some of its spectra unusable. Hence, there is an opportunity to utilize machine learning for imputing the missing spectra. This study aims to compare the performance of known imputation methods for spectrometry-based data such as Random Forest, Quantile Regression Imputation of Left-Censored Data (QRILC), and K-Nearest Neighbour (KNN) with alternative imputation methods that involve weights that incorporate the spatial component of where on the tissue the spectra are measured. The results reveal that spatial imputation methods outperform the regular imputation method. This implies that there is a spatial relationship between spectra in a Raman spectroscopy matrix where spectra that are closer are more correlated than spectra that are further away.
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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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".