Qualitative Techniques for Generating Spatial Prior Information for Biomedical Microwave Imaging
Bibliographic record
Abstract
The use of quantitative microwave imaging for biomedical applications represents one of its most relevant application areas due to the specificity of the complex-valued permittivity with regard to differentiating normal and diseased anatomical tissues. The success of such quantitative methods relies on improving their reconstruction accuracy and resolution. In the following we propose the use of two qualitative imaging methods, the linear sampling and the orthogonality sampling methods, to generate spatial priors that are used as a numerical inhomogeneous background medium within the (quantitative) contrast source inversion scheme. Both qualitative imaging methods are able to create morphological maps, in almost real-time, from the same microwave scatteredfield data. The resulting quantitative reconstructions show improvements in both accuracy and resolution, compared to blind reconstruction, and thus the combined technique represents a significant contribution towards the design of simpler and low-cost imaging systems.
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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".