The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Typical examples include Transpath [148] and CS-CO [149] for histopathological image analysis, which reduced the costs for histopathological image annotations.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [148]
In the citing paper
“The remaining studies explored multiple other tasks and many focused on classification of malignant disorders such as breast cancer56,61,88, skin cancer89, and lung cancer59.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [61]
Citations
Cites
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415
Bottleneck Transformers for Visual Recognition
Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082
Li, B., Li, Y., & Eliceiri, K. W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01409 · Cited by 957
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101544 · Cited by 649
Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations
Koohbanani, N. A., Unnikrishnan, B., Khurram, S. A., Krishnaswamy, P., & Rajpoot, N. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3056023 · Cited by 188
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102256 · Cited by 116
Self-Supervision Closes the Gap Between Weak and Strong Supervision in HistologyPreprint
Dehaene, O., Camara, A., Moindrot, O., de Lavergne, A., & Courtiol, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03583 · Cited by 83
Lu, M. Y., Chen, R. J., Wang, J., Dillon, D., & Mahmood, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10825 · Cited by 80
Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology
Mormont, R., Geurts, P., & Marée, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2992878 · Cited by 66
Commons, R. J., Simpson, J. A., Thriemer, K., Abreha, T., Adam, I., Anstey, N. M., Assefa, A., Awab, G. R., Baird, J. K., Barber, B. E., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002928 · Cited by 54
Lu, M. Y., Chen, R. J., & Mahmood, F. (2020) · SPIE Medical Imaging: Digital Pathology · doi:10.1117/12.2549627 · Cited by 49
Research works printed but not linked: 4
- Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020) · arXiv
- Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016) · arXiv
- Wei, J., et al.: A petri dish for histopathology image analysis. arXiv preprint arXiv:2101.12355 (2021) · arXiv
- Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677 (2020) · arXiv
Cited by
Sorted by citations
Transformer-based unsupervised contrastive learning for histopathological image classification
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102559 · Cited by 751
Self-citationTransformers in medical image analysis
He, K., Chen, G., Li, Z., Rekik, I., Yin, Z., Wen, J., Gao, Y., Wang, Q., Zhang, J., & Shen, D. G. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.07.002 · Cited by 604
Self-citationHuang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 454
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187
Metadata
- Authors
- Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Huang, J., Yang, W., & Han, X.
- Affiliations
- Sichuan University (CN)
- Tencent (China) (CN)
- Corresponding author
- Zhang, Jun
- Zhang, Jing
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 186-195
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3203168750
- Cited by
- 178
- References
- 18
- Citations by year
- 2026: 17 · 2025: 42 · 2024: 45 · 2023: 49 · 2022: 23 · 2021: 2
- FWCI
- 13.75
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- histopathological image classification, self-supervised learning, vision transformer, CNN-Transformer hybrid, pre-training, limited annotations, local and global feature extraction, transfer learning
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 ›
Are you an author, or do you see a problem with this page? Request a review