PGDIPS: Physics-guided deep image prior enables general stain deconvolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Perform stain deconvolution on a histopathology images. We have provided few images with different combinations of staining. These experiments can be performed by running the capsule with different configuration files, which can be passed by python3 pgdips.py config=he.config (or ihc.cfg, alcianblue.cfg, DeepLIIF6.cfg, fibronectin.cfg, thrichrome.cfg, SMACD34.cfg). The default setting is for H&E image deconvolution. For more information please refer to our preprint (https://www.biorxiv.org/content/10.1101/2022.12.06.519385v1), code repository (https://github.com/GJiananChen/PGDIPS) or a webUI where you could upload your own histopathology image and use it in a docker (https://www.codido.co/marketplace/browse).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.029 |
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 it