Editorial: Recent trends and advancements in multispectral and hyperspectral imaging for cancer detection
Bibliographic record
Abstract
Hyperspectral imaging (HSI) acquire images throughout numerous spectral bands, 14 encompassing visible wavelengths (380 nm-700 nm), near-infrared (800 nm-2500 nm), and 15 mid-infrared (2500 nm-15000 nm) [1,2]. Each pixel contains several spectral bands to obtain 16 specific information regarding certain pollutants [3]. HSI acquires a data cube that encompasses 171D spectral and 2D spatial information [4,5]. A one-dimensional spectrum illustrates the 18 absorption of light by tissues at a pixel across various wavelengths, while two-dimensional 19 vectors denote each pixel's position within the spectral spatial array [6]. HSI has been employed 20 in numerous biomedical applications to assess the physical properties of intricate surfaces and 21 identify cancer cells through precise spectral signatures [7]. We sincerely thank all the authors who contributed to this Research Topic. 95
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".