Enhancement of Gauss–Newton Inversion Method for Biological Tissue Imaging
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Bibliographic record
Abstract
The multiplicatively regularized Gauss-Newton inversion (GNI) algorithm is enhanced and utilized to obtain complex permittivity profiles of biological objects-of-interest. The microwave scattering data is acquired using a microwave tomography system comprised of 24 co-resident antennas immersed into a saltwater matching fluid. Two types of biological targets are imaged: ex vivo bovine legs and in vivo human forearms. Four different forms of the GNI algorithm are implemented: a blind inversion, a balanced inversion, a shape-and-location inversion, and a novel balanced shape-and-location inversion. The latter three techniques incorporate typical permittivity values of biological tissues as prior information to enhance the reconstructions. In those images obtained using the balanced shape-and-location reconstruction algorithm, the various parts of the tissue being imaged are clearly distinguishable. The reconstructed permittivity values in the bovine leg images agree with those obtained via direct measurement using a dielectric probe. The reconstructed images of the human forearms qualitatively agree with magnetic resonance imaging images, as well as with the expected dielectric values obtained from the literature.
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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 it