An Open-Access Experimental Dataset for Breast Microwave Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microwave imaging has shown potential for breast cancer screening, but further evaluation of the clinical viability of breast microwave imaging (BMI) systems is required. Previous phantom studies have shown promise, but after decades of BMI research, simulation studies still dominate. This work addresses the challenges of small sample sizes and a lack of experimental data by providing an open-source experimental dataset, obtained using a pre-clinical BMI system. The University of Manitoba BMI Dataset (UM-BMID) contains data from 1257 phantom scans. UM-BMID is publicly available, and the community is encouraged to use it for large-scale BMI analysis. The application of logistic regression for tumor-detection on a subset of the dataset was studied to demonstrate one use of UM-BMID. The diagnostic accuracy of the classifier was (85 ± 4)%, demonstrating the promise of machine learning methods for tumor-detection in BMI.
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.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 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