Self-supervised Learning for Segmentation in Two-Photon Fluorescence Microscopy
Bibliographic record
Abstract
Developing deep learning models for microscopy analysis is challenging due to the lack of labeled training data. Self-supervised learning (SSL), which leverages unlabeled data to improve downstream performance, may be beneficial to streamline the annotation of two-photon fluorescence microscopy (TPFM) data. We developed a pipeline using SSL to assess whether unlabeled data can improve the segmentation accuracy of neurons and vessels in TPFM. We devised four pretext tasks, including shuffling, rotation, axis rotation, and reconstruction, to train models without supervision using the U-Net architecture. We introduced the neighborhood density metric to assess model performance that is more sensitive to downstream analyses than typical overlap metrics. We examined our pipeline by finetuning pretrained models on noisy data and by removing crosstalk. We applied our framework to multiple datasets, including out-of-distribution data, demonstrating that SSL-trained models outperform fully-supervised models on segmentation tasks and are more robust to noisy distributions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".