A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models

An image is worth 16x16 words: Transformers for image recognition at scale URL https://arxiv.org/abs/2010.11929

Original

Dosovitskiy, A. et al · 2010

Earlier work this paper cites.

Comprehensive molecular profiling of lung adenocarcinoma

Network, C. G. A. R. et al · 2014

Earlier work this paper cites.

Scaling self-supervised learning for histopathology with masked image modeling

Filiot, A. et al · 2023

Cited alongside, same era.

Deep learning 521

LeCun, Y., Bengio, Y. & Hinton, G

Cited in the paper.

Transformer-based unsupervised contrastive learning for histopathological image classification 81

Wang, X. et al

Cited in the paper.

An empirical study of training self-supervised vision transformers URL https://arxiv.org/abs/2104.02057

Original

Chen, X., Xie, S. & He, K

Cited in the paper.

Swin transformer: Hierarchical vision transformer using shifted windows URL https://arxiv.org/abs/2103.14030

Original

Liu, Z. et al

Cited in the paper.

The cancer genome atlas pan-cancer analysis project 45

The Cancer Genome Atlas Research Network et al

Cited in the paper.

iBOT: Image BERT pre-training with online tokenizer URL https://arxiv.org/abs/2111.07832

Original

Zhou, J. et al

Cited in the paper.

A general-purpose self-supervised model for computational pathology URL https://arxiv.org/abs/2308.15474

Original

Chen, R. J. et al

Cited in the paper.

Towards a general-purpose foundation model for computational pathology 30

Chen, R. J. et al

Cited in the paper.

DINOv2: Learning robust visual features without supervision URL https://arxiv.org/abs/2304.07193

Original

Oquab, M. et al

Cited in the paper.

Virchow: A million-slide digital pathology foundation model URL https://arxiv.org/abs/2309.07778

Original

Vorontsov, E. et al

Cited in the paper.

Computational pathology at health system scale – self-supervised foundation models from three billion images URL https://arxiv.org/abs/2310.07033

Original

Campanella, G. et al

Cited in the paper.

Longnet: Scaling transformers to 1,000,000,000 tokens

Original

Ding, J. et al · 2023

Later among the works it cites.