Lightweight self supervised learning framework for domain generalization in histopathology
Bibliographic record
Abstract
The emergence of large foundation models (FMs) in histopathology, trained on extensive image datasets using high-performance graphics processing unit (GPU) clusters, has demonstrated significant potential in advancing computational pathology. FMs have potential to overcome the domain gap between training and testing datasets, which creates more translation opportunities. However, the reliance on vast computational resources and large-scale data often limits accessibility and widespread adoption of FMs. To address this limitation, we present HistoLite, a lightweight self-supervised learning framework designed to enable domain-invariant representation learning in histopathology. HistoLite utilizes customizable auto-encoders within a self-supervised learning paradigm that learns generalized and transferable features in an efficient manner. We evaluated the proposed framework using breast Whole Slide Images (WSIs) and benchmarked performance with state-of-the-art FMs for domain generalization. A novel dataset was curated that is of the same tissue slides, scanned by two different scanning platforms, which allows for specific analysis of covariate shifts due to scanner bias. Aspects evaluated include the difference in embeddings across scanners using novel representation shift metrics, including a robustness index, and accuracy, which looks at performance on downstream tasks. The top performing models were UNI, Virchow2 and Prov-GigaPath, likely due to large model sizes and training datasets. In general, most FMs were found to be susceptible to scanner-bias, as shown by differences in embeddings and drop in performance on the held-out scanner. This has significant implications for real-world deployment of FMs in histopathology. HistoLite offered low representation shift in embeddings, the lowest performance drop on out-of-domain data with modest classification accuracy, indicating the smaller model may exhibit a tradeoff between accuracy and generalization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".