Challenges with Machine Learning for Microwave Breast Tumor detection
Bibliographic record
Abstract
In this paper, challenges of combining machine learning techniques with near-field microwave probes for breast tumor detectionis presented. The concept of using microwaves imaging (MI) modality for breast tumors detection is based on the electrical propertiescontrast between normal and tumors breast tissues. MI utilizedmicrowave signals to illuminate the breast tissues using near fieldprobes placed at different locations surrounding the breast. Thebackscattered microwaves signals are then received by the sameprobes. Diagnosis breast tumor is done by estimating the variations in the response of the reflection coefficient of the probe. Machine learning techniques are applied to accentuate the variancein the sensor’s responses for both healthy and tumorous cases.The main challenge of using the machine learning technique withnear-field microwave probes for breast tumor detection is to find asuitable combination of features and classifiers which discriminatesbetween the normal and abnormal breast.
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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".