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Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images.
Regression models and life-tables
Cox, D. R · 1972
Earlier work this paper cites.
Evaluating the yield of medical tests
Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., and Rosati, R. A · 1982
Earlier work this paper cites.
Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology
Heindl, A., Nawaz, S., and Yuan, Y · 2015
Earlier work this paper cites.
Improved bounds on the dot product under random projection and random sign projection
Kaban, A · 2015
Earlier work this paper cites.
Review the cancer genome atlas (tcga): an immeasurable source of knowledge
Tomczak, K., Czerwińska, P., and Wiznerowicz, M · 2015
Earlier work this paper cites.
Spatial heterogeneity in the tumor microenvironment
Yuan, Y · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A · 2017
Earlier work this paper cites.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J., and Welling, M · 2018
Earlier work this paper cites.
Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network
Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y · 2018
Earlier work this paper cites.
Computing receptive fields of convolutional neural networks
Araujo, A., Norris, W., and Sim, J · 2019
Earlier work this paper cites.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., and Fuchs, T. J · 2019
Earlier work this paper cites.
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., and Gurcan, M. N · 2019
Earlier work this paper cites.
Multiple instance learning with center embeddings for histopathology classification
Chikontwe, P., Kim, M., Nam, S. J., Go, H., and Park, S. H · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A · 2020
Earlier work this paper cites.
Bias in cross-entropy-based training of deep survival networks
Zadeh, S. G. and Schmid, M · 2020
Earlier work this paper cites.
Artificial intelligence and computational pathology
Cui, M. and Zhang, D. Y · 2021
Earlier work this paper cites.
Dt-mil: deformable transformer for multi-instance learning on histopathological image
Li, H., Yang, F., Zhao, Y., Xing, X., Zhang, J., Gao, M., Huang, J., Wang, L., and Yao, J · 2021
Earlier work this paper cites.
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F., Chen, T. Y., Chen, R. J., Barbieri, M., and Mahmood, F · 2021
Earlier work this paper cites.
Transmil: Transformer based correlated multiple instance learning for whole slide image classification
Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al · 2021
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Deep learning in histopathology: the path to the clinic
Van der Laak, J., Litjens, G., and Ciompi, F · 2021
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Nyströmformer: A nyström-based algorithm for approximating self-attention
Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., and Singh, V · 2021
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Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides
Xu, F., Zhu, C., Tang, W., Wang, Y., Zhang, Y., Li, J., Jiang, H., Shi, Z., Liu, J., and Jin, M · 2021
Cited alongside, same era.
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., and Mahmood, F · 2022
Cited alongside, same era.
Artificial intelligence for digital and computational pathology
Song, A. H., Jaume, G., Williamson, D. F., Lu, M. Y., Vaidya, A., Miller, T. R., and Mahmood, F · 2023
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When an image is worth 1,024 x 1,024 words: A case study in computational pathology
Wang, W., Ma, S., Xu, H., Usuyama, N., Ding, J., Poon, H., and Wei, F · 2023
Later among the works it cites.
Xiong, C., Chen, H., Sung, J. J., and King, I · 2023
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Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction
Xu, Y. and Chen, H · 2023
Later among the works it cites.
Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., and Ré, C · 2022
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Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis
Hou, W., Yu, L., Lin, C., Huang, H., Yu, R., Qin, J., and Wang, L · 2022
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Scl-wc: Cross-slide contrastive learning for weakly-supervised whole-slide image classification
Wang, X., Xiang, J., Zhang, J., Yang, S., Yang, Z., Wang, M.-H., Zhang, J., Yang, W., Huang, J., and Han, X · 2022
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Dsnet: A dual-stream framework for weakly-supervised gigapixel pathology image analysis
Xiang, T., Song, Y., Zhang, C., Liu, D., Chen, M., Zhang, F., Huang, H., O’Donnell, L., and Cai, W · 2022
Cited alongside, same era.
Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification
Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S. E., and Zheng, Y · 2022
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A graph-transformer for whole slide image classification
Zheng, Y., Gindra, R. H., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., and Kolachalama, V. B · 2022
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
Cited alongside, same era.
Yu, J.-G., Wu, Z., Ming, Y., Deng, S., Li, Y., Ou, C., He, C., Wang, B., Zhang, P., and Wang, Y · 2023
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Cross-modal translation and alignment for survival analysis
Zhou, F. and Chen, H · 2023
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Towards a general-purpose foundation model for computational pathology
Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al · 2024
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Histgen: Histopathology report generation via local-global feature encoding and cross-modal context interaction
Guo, Z., Ma, J., Xu, Y., Wang, Y., Wang, L., and Chen, H · 2024
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A visual-language foundation model for computational pathology
Lu, M. Y., Chen, B., Williamson, D. F., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., et al · 2024
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Towards a generalizable pathology foundation model via unified knowledge distillation
Ma, J., Guo, Z., Zhou, F., Wang, Y., Xu, Y., Cai, Y., Zhu, Z., Jin, C., Jiang, Y. L. X., Han, A., et al · 2024
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Feature re-embedding: Towards foundation model-level performance in computational pathology
Tang, W., Zhou, F., Huang, S., Zhu, X., Zhang, Y., and Liu, B · 2024
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A multimodal knowledge-enhanced whole-slide pathology foundation model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Yang, S., Lin, H., Wang, X., Wang, J., Liang, L., Han, A., et al · 2024
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Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology
Yang, S., Wang, Y., and Chen, H · 2024
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Beyond h&e: Unlocking pathological insights with polarization via self-supervised learning
Du, Y., Zhuang, J., Zheng, X., Cong, J., Guo, L., He, C., Luo, L., and Li, X · 2025
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Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification
Guo, Z., Xiong, C., Ma, J., Sun, Q., Feng, L., Wang, J., and Chen, H · 2025
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Any-to-any learning in computational pathology via triplet multimodal pretraining
Sun, Q., Guo, Z., Peng, R., Chen, H., and Wang, J · 2025
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Diffusion-based virtual staining from polarimetric mueller matrix imaging
Zheng, X., Wen, J., Zhuang, J., Du, Y., Cong, J., Guo, L., He, C., Luo, L., and Chen, H · 2025
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