High‐resolution quantitative acoustic microscopy of cutaneous carcinoma and melanoma: Comparison with histology
Bibliographic record
Abstract
BACKGROUND: The increased incidence rate of skin cancers during the last decades is alarming. One of the significant difficulties in the histopathology of skin cancers is appearance variability due to the heterogeneity of diseases or tissue preparation and staining process. This study aims to investigate whether the high-resolution acoustic microscopy has the potential for identifying and quantitatively classifying skin cancers. MATERIAL/METHODS: Unstained standard formalin-fixed skin tissue samples were used for ultrasonic examination. The high-frequency acoustic microscope equipped with the 320 MHz transducer was utilized to visualize skin structure. Fourier transform was performed to calculate the sound speed and attenuation in the tissue. RESULTS: The acoustic images demonstrate good concordance with the traditional histology images. All histological features in the tumour were easily identifiable on acoustic images. Each skin cancer type has its combination of ultrasonic properties significantly different from the healthy skin. CONCLUSIONS: High-resolution acoustic imaging strengthened with quantitative analysis shows a potential to work as an auxiliary imaging modality assisting pathologists to lean to the particular decision in doubtful cases. The method can also assist surgeon to ensure the complete resection of a tumour.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".