Self-supervised deep metric learning for prototypical zero-shot lesion retrieval in placenta whole-slide images
Bibliographic record
Abstract
Postnatal adverse outcomes can often be explained and predicted by the pathological evaluation of the placenta after a pregnancy. However, placenta whole-slide image (WSI) analysis is not performed systematically due to the specialized skills required. There is no public dataset available for placenta WSIs and precise annotations on private datasets are very limited. Furthermore, we show that in this context of low data regime and scarcity of expert annotations, current computational pathology foundation models struggle to generalize to the specific case of the placental tissue. We propose a new deep metric learning (DML)-based method for efficient inflammatory lesion retrieval in placenta WSIs in very low data settings. We train a feature extractor without labels by adapting an existing self-supervised learning framework to the DML problem setting. Once trained, the feature extractor is used to define prototype vectors for inflammatory lesions, using a very limited number of known pathological patches extracted from a single placenta. We can then retrieve inflammatory lesions in unseen WSIs by comparing patches with prototype vectors in the feature extractor's metric space. The similarity map thus obtained is then refined using a simple post-processing method to take into account spatial patch proximity. We evaluated our method on a private dataset of 165 annotated WSIs (51 placentas) and on the CAMELYON16 dataset for lymph node metastasis retrieval. We achieved a patch-level AUROC of 0.978 on our dataset and 0.928 on CAMELYON16 in the zero-shot setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".