Bibliographic record
Abstract
Sensors based on complementary metal oxide semiconductors (CMOS) technology have recently been considered for mammography applications. CMOS offers the advantages of lower cost and relative ease of fabrications. We report on the evaluation of a CMOS imager (C9730DK, Hamamatsu Corporation) with 14-bit digitization and 50-micron detector element (del) resolution. The imager has an active area of 5 x 5 cm and uses 160-micron layer of needle-crystal CsI (55 mg/cc) to convert x-rays to light. The detector is suitable for spot and specimen imaging and image-guided biopsy. To evaluate resolution performance, we measured the modulation transfer function (MTF) using the slanted edge method. We also measured the normalized noise power spectrum (NNPS) using Fourier analysis of uniform images. The MTF and NNPS were used to determine the detective quantum efficiency (DQE) of the detector. The detector was characterized using a molybdenum target/molybdenum filter mammography x-ray source operated at 28 kVp with 44mm of PMMA added to mimic clinical beam quality (HVL = 0.62 mm Al). Our analysis showed that the imager had a linear response. The MTF was 28% at 5 lp/mm and 8% at 10 lp/mm. The product of the NNPS and exposure showed that the detector was quantum limited. The DQE near 0 lp/mm was in the 55-60% range. The DQE and MTF performance of the CMOS detector are comparable to published values for other digital mammography detectors.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".