Fine-Grained Classification for Depth Estimation From Monocular Microscopy for Robotic Micromanipulation of Motile Cells
Bibliographic record
Abstract
Manipulation of motile cells is crucial for biological research and clinical applications. However, obtaining Z-axis visual feedback under monocular microscopy remains a challenge for robotic micromanipulation. Traditional depth-from-focus and depth-from-defocus methods fail to handle motile cells due to time-consuming focus search or inaccurate defocus modeling. This paper addresses these limitations by reformulating depth estimation as a fine-grained multi-class depth classification problem that exploits the shallow depth-of-field characteristic of microscopy. We propose a Fine-Grained Attention Fusion Module (FGAF-Module) that combines multi-scale grouped convolution for extracting subtle depth-related features with attention mechanisms to focus on discriminative regions in cell images. Additionally, channel-based feature augmentation methods, including CrossNorm and SelfNorm, enhance fine-grained feature discrimination while improving model generalization to handle morphological variations during cell movement. A weighted loss function further guides the model to distinguish between adjacent depth categories by penalizing errors proportionally to depth differences. For network training evaluation, the FGAF-module enhanced network achieved 83.52% top-1 classification accuracy and 96.88% top-3 classification accuracy while maintaining real-time performance at 90 frames per second. To demonstrate the capability of our approach in providing visual feedback for robotic manipulation of motile cells, the trained depth estimation model was integrated into a robotic sperm aspiration system. The model provided real-time visual depth feedback to guide 3D pipette localization during sperm aspiration procedures, achieving a 92% success rate for live motile sperm aspiration. These results validate the effectiveness of fine-grained classification for monocular depth estimation in micromanipulation applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".