Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition
Bibliographic record
Abstract
Abstract We introduce a deep learning model that predicts super-resolved versions of diffraction-limited microscopy images. Our model, named Task- Assisted Generative Adversarial Network (TA-GAN), incorporates an auxiliary task (e.g. segmentation, localization) closely related to the observed biological nanostructures characterization. We evaluate how TA-GAN improves generative accuracy over unassisted methods using images acquired with different modalities such as confocal, brightfield (diffraction-limited), super-resolved stimulated emission depletion, and structured illumination microscopy. The generated synthetic resolution enhanced images show an accurate distribution of the F-actin nanostructures, replicate the nanoscale synaptic cluster morphology, allow to identify dividing S. aureus bacterial cell boundaries, and localize nanodomains in simulated images of dendritic spines. We expand the applicability of the TA-GAN to different modalities, auxiliary tasks, and online imaging assistance. Incorporated directly into the acquisition pipeline of the microscope, the TA-GAN informs the user on the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to optimize the acquisition sequence, and reduce light exposure. The TA-GAN also enables the creation of domain-adapted labeled datasets requiring minimal manual annotation, and assists microscopy users by taking online decisions regarding the choice of imaging modality and regions of interest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".