Comparative Evaluation of Deep Learning Architectures for Retinal Ganglion Cell Counting: FCRN-A, FCRN-A-v2, and U-Net
Bibliographic record
Abstract
Deep Learning (DL) has revolutionized healthcare, particularly in disease prediction, medical imaging, and drug discovery. In ophthalmology, DL facilitates cell counting by detecting and quantifying retinal ganglion cells (RGCs) from microscopy images. Manual counting is labor-intensive and errorprone, leading to the development of automated DL systems. This paper compares three architectures, namely, Fully Convolutional Regression Network FCRN-A, FCRN-A-v2, and U-Net, using a synthetic dataset and a custom real dataset. The networks, trained through supervised learning, produce density maps, with model performance evaluated via 5-fold cross-validation based on mean absolute error (MAE) and standard deviation (STD). Results show U-Net outperforms the others, achieving the lowest MAE and STD in cell counting.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".