Experimental Evaluation of Composite Tissue-Type Ultrasound and Microwave Imaging
Bibliographic record
Abstract
The recently proposed concept of composite tissue-type image (cTTI), which provides an easy-to-interpret image constructed from quantitative ultrasonic and electromagnetic properties, is experimentally investigated. The experimental data set used for the ultrasound investigation is obtained from the multimodal ultrasound breast imaging system. The experimental data set utilized for microwave imaging is provided by an inhouse system at the University of Manitoba. To this end, a tissue mimicking phantom and a human forearm are utilized for experimental ultrasound and microwave imaging. In addition, the cTTI algorithm is modified to take into account differences in the quantitative accuracy of reconstructing one property compared to other properties so as to increase the achievable accuracy in the resulting cTTI. In addition to experimental ultrasound and microwave data, the cTTI method is also applied against synthetic data obtained from an MRI-based numerical breast phantom to further demonstrate the performance of the cTTI not only with respect to microwave and ultrasound tomography data but also with respect to their combination. Finally, the improvements of the cTTI reconstructions based on changing the prior probabilities compared with equal prior probability distribution are shown for combined ultrasound and microwave tomography of a numerical breast phantom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".