Finger Models: Example of Complex Geometry in Investigations on Microwave Skin Cancer Diagnosis
Bibliographic record
Abstract
Micro/mm-wave technology is a promising tool for the early diagnosis of skin cancers. It is important that the models, whether for numerical or experimental phantom investigations, be a good anatomical representation of the tissue distribution. Most models for skin cancer diagnoses consider layered media (skin, fat) with tumor inclusions. However, the human body has a number of extremities of a more complex geometry. To begin addressing this challenge, we have selected the human finger as an excellent model of complex tissue distribution, possibly all within the sensing depth of the probe placed on the skin surface. Four finger models, with progressive complexity of tissue layers, are simulated by following the anatomical finger structure. The models include a slim probe, applied to the healthy structure and its version with basal cell carcinoma (BCC) or squamous cell carcinoma (SCC) at 20 GHz - 50 GHz. We then observe the S-parameters, showing that, for meaningful results, finger model should consist of at least three layers: skin, fat, and ligament in microwave probe sensing for BCC/SCC detection. These results suggests that, for body portions with complex tissue distribution close to the skin surface, all tissues must be included in the model in order to properly assess the feasibility of the microwave-based diagnostic devices.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".