Learning to autofocus in whole slide imaging via physics-guided deep cascade networks
Bibliographic record
Abstract
Whole slide imaging (WSI), is an essential technology for digital pathology, the performance of which is primarily affected by the autofocusing process. Conventional autofocusing methods either are time-consuming or require additional hardware and thus are not compatible with the current WSI systems. In this paper, we propose an effective learning-based method for autofocusing in WSI, which can realize accurate autofocusing at high speed as well as without any optical hardware modifications. Our method is inspired by an observation that sample images captured by WSI have distinctive characteristics with respect to positive / negative defocus offsets, due to the asymmetry effect of optical aberrations. Based on this physical knowledge, we develop novel deep cascade networks to enhance autofocusing quality. Specifically, to handle the effect of optical aberrations, a binary classification network is tailored to distinguish sample images with positive / negative defocus. As such, samples within the same category share similar characteristics. It facilitates the followed refocusing network, which is designed to learn the mapping between the defocus image and defocus distance. Experimental results demonstrate that our method achieves superior autofocusing performance to other related methods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".