Nonlinear microscopy and deep learning classification for mammary glandmicroenvironment studies
Bibliographic record
Abstract
Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.
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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 | 0.000 | 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) | 0.000 | 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".