Tissue-Type Classification With Uncertainty Quantification of Microwave and Ultrasound Breast Imaging: A Deep Learning Approach
Bibliographic record
Abstract
A deep learning approach is proposed for performing tissue-type classification of tomographic microwave and ultrasound property images of the breast. The approach is based on a convolutional neural network (CNN) utilizing the U-net architecture that also quantifies the uncertainty in the classification of each pixel. Quantitative tomographic reconstructions of dielectric properties (complex-valued permittivity), ultrasonic properties (compressibility and attenuation), as well as their combination, with the corresponding actual tissue-type classification constitute the training set. The CNN learns to map the quantitative property reconstructions to a single tissue-type image. The level of confidence in predicting a tissue-type at each pixel is determined. This uncertainty quantification is diagnostically critical for biomedical applications, especially when attempting to distinguish between cancerous and healthy tissues. The Gauss-Newton Inversion algorithm is used for the quantitative reconstruction of both dielectric and ultrasonic properties. Electromagnetic and ultrasound scattered-field data is obtained from MRI-derived numerical breast phantoms. Several numerical breast phantoms types, from fatty to dense, are considered. The proposed classification and uncertainty quantification approach is shown to outperform a previously studied tissue-type classification method based on a Bayesian approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".