Breast tissue characterization with high-frequency scanning acoustic microscopy
Bibliographic record
Abstract
We have performed imaging of breast tissue using scanning acoustic microscopy (SAM) in the range of 25–50 MHz with the goal of accurately and rapidly determining the structure and composition throughout the volume of the samples. In contrast to traditional histological slides, SAM images can be obtained without special preparation, sometimes even without sectioning, but with sufficiently high spatial resolution to give information comparable to surface optical images. As a result, the use of high-frequency SAM at the time of breast lumpectomy to identify disease-free margins has the potential to reduce reoperative rates, patient anxiety, and local recurrence. However, only limited work has been performed to characterize breast tissue in the frequency range above clinical ultrasound devices. The samples are 4-cm2-thick sections (2–3 mm) taken from mastectomies and preserved in formalin. They are placed between two plates and immersed in water during imaging. Attenuation images are acquired by focusing the acoustic beam at the top and bottom of the samples, although better results were obtained for bottom focusing. For purposes of comparison and identification of histological features, acoustical images will be presented along with optical images obtained from the same samples. [Work supported by CIHR.]
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".