Microwave breast tumor detection exploiting wideband Jacobians
Bibliographic record
Abstract
Gradient-based iterative algorithms are preferred in microwave tomography due to their fast convergence despite the fact that the solution is only local. The drawback is that response Jacobians are not available with current commercial electromagnetic solvers and reconstruction algorithms have to be equipped with in-house codes specifically designed to produce response derivatives in addition to the responses themselves. Even with in-house codes, the computation of the response Jacobians in a wide frequency band is a challenge. Here, we present an efficient approach to the computation of wideband response Jacobians with time-domain solvers. Through this approach, the Jacobian distributions (maps) of imaged regions can be computed with relatively small memory requirements and negligible computational overhead. This self-adjoint computation is reduced to a simple post-process of the field solution which can be applied with any commercial FDTD-based solver. We show that the detection of scatterers such as breast tumors can be achieved with a single system analysis by computing the wideband Jacobian maps of high-fidelity patient-specific tissue models.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".