Exploring CNN-Based Self-Supervised Illumination Inhomogeneity Compensation for Serial Optical Coherence Tomography
Bibliographic record
Abstract
Serial blockface histology is a 3D imaging modality that combines a vibratome with a microscope. Whole samples are acquired by sequentially removing small tissue layers with the vibrating blade and by generating a mosaic of several images of the revealed tissue which can be assembled to obtain a 3D representation of the sample at a high resolution. Due to many factors, the acquired mosaic tiles can be affected by complex illumination inhomogeneity that negatively affects the data reconstruction and analysis. Here, we propose a convolutional neural network approach to estimate and compensate the illumination inhomogeneity. The model is trained with simulated vignettes without using illumination ground truth, which is many times harder or even impossible to obtain. Using a small multiresolution dataset consisting in serial OCT images from whole mouse brains, we show that our proposed approach has many advantages compared to an unsupervised a posteriori illumination compensation method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".