Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Open accessAbstract
Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning (SSL) has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements. We release the pre-trained model weights11https://lunit-io.github.io/research/publications/pathology_ssl.
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The dataset consists of clinical data generated during standard hospital operations from three health systems.
“In contrast to previous efforts36,41, the dataset consists of clinical data generated during standard hospital operations from three health systems.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [36]
Using SSL to train image encoders on unlabeled pathology data is superior to relying on models pretrained on other domains such as natural images.
“It is becoming abundantly clear that using SSL to train image encoders on unlabeled pathology data is superior to relying on models pretrained on other domains such as natural images26,36,37.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [36]
Evidence emerged that DINO tends to outperform contrastive learning and masked image modeling approaches for pathology pretraining.
“While evidence emerged that DINO tends to outperform contrastive learning and masked image modeling approaches for pathology pretraining26,36, there is no direct comparison of DINO and DINOv2.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [36]
Virchow is the largest foundation model for computational pathology to date.
“we present Virchow, the largest foundation model for computational pathology to date.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [39]
These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.
“These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [39]
Recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters.
“Despite the challenges in collecting large-scale datasets in the pathology domain, recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters35–42”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [39]
Self-supervised learning is enabling in-domain transfer by removing the label requirement, driving a second wave of scaling to tens of thousands of WSIs.
“Now self-supervised learning is enabling in-domain transfer by removing the label requirement, driving a second wave of scaling to tens of thousands of WSIs to inform image representation35–39,56.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [39]
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- Authors
- Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S.
- Affiliations
- Lunit (South Korea) (KR)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 3344-3354
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076317
- Cited by
- 187
- References
- 59
- Citations by year
- 2026: 35 · 2025: 81 · 2024: 64 · 2023: 7
- FWCI
- 33.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- self-supervised learning, computational pathology, domain-specific pre-training, nuclei instance segmentation, unlabeled data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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