Classification of infrared spectra from skin tumors
Bibliographic record
Abstract
The clinical differential diagnosis of skin tumors is an often-challenging task, to which the probing of skin with mid- and near-infrared (IR) light may be contributory. The development of objective methods for the analysis of IR spectra remains a major hurdle to developing clinically useful applications. The authors highlight different processing methods for IR spectra from skin biopsies and in-vivo skin tumors. Spectroscopic maps of biopsies of basal cell, squamous cell and melanocytic neoplasms were objectively grouped into distinct clusters that corresponded with tumor, epidermis, dermis, follicle and fat. Normal and abnormal skin components were located within maps using a search engine based upon linear discriminant analysis (LDA). In all instances, areas of tumor were distinct from normal tissue in biopsies. In-vivo, near-IR spectroscopy and LDA allowed discrimination between benign and malignant skin lesions with a high degree of accuracy. We conclude that IR spectroscopy has significant diagnostic promise in the skin cancer arena. The analytical methods described can now be used to create a powerful classification scheme in which to detect skin tumor cells within biopsied and living skin.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".