Automated Workflow for Evaluating Microwave and Multi-Modality Breast Images
Bibliographic record
Abstract
The emergence and subsequent expansion of the field of medical microwave imaging has resulted in numerous approaches to image reconstruction. This includes microwave tomography, radar imaging, and more recently, multi-modality approaches. However, there is an absence of a standardized and widely accepted process that is proficient at extracting information from these images and employing this knowledge to conduct a thorough quantitative evaluation of images and regions within images. This shortcoming may interfere with a researcher's ability to make reliable and consistent inferences from experiments and to interpret results. Consequently, comparing the results of different research groups is difficult. This is becoming increasingly relevant due to the development of standardized test phantoms and the increase in clinical studies. To remedy this deficiency, an automated workflow has been developed with the objective to standardize the processing and analysis of images acquired from a range of modalities. Images are first segmented into regions dominated by a tissue type. Quantitative information extracted from these regions is used for analysis and by visualization tools for the qualitative interpretation of images. The effectiveness of the workflow is demonstrated with multiple examples that focus on quantifying changes to images due to enhancements of the reconstruction algorithm or perturbations of a parameter used by the reconstruction operator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".