From preprocessing to fuzzy classification of IR images of paraffin embedded cancerous skin samples
Bibliographic record
Abstract
Mid-infrared (IR) micro-spectral imaging is an efficient method to analyze molecular composition of biomedical samples. In clinical oncology, this non-invasive technique is generally used on frozen biopsies to localize and diagnose cancerous tissues in their early stages. However, samples are usually fixed in paraffin in order to be preserved from decay, but the IR signature of paraffin prevents the study of the underlying tissue. To neutralize the paraffin signal from the recorded data, preprocessing methods based on independent component analysis (ICA) and nonnegatively constrained least squares (NCLS) or on extended multiplicative signal correction (EMSC) have been recently developed. Then, in order to identify tumor areas, clustering techniques are applied on the preprocessed data, the final result being a false-color map of the biomedical sample which is comparable to the conventional histological image. By allowing each recorded spectrum to be assigned to every cluster, the fuzzy clustering gives more realistic results for unclear tissue boundaries by better highlighting the tumor and peritumoral areas. A recent algorithm based on the redundancy of classes allows to automatically estimate the optimal number of classes and the optimal fuzzy parameter. In this paper, we analyze the effects of the preprocessing methods on the optimal parameter extraction and on the results of the fuzzy clustering on different paraffin embedded cancerous skin samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".