2000 character limit reached
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis (2405.17446v3)
Published 20 May 2024 in eess.IV and cs.CV
Abstract: The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for this task utilize a ResNet50 backbone pre-trained on natural images. By leveraging recenetly released histopathological foundation models such as UNI and Hibou, the predictive prowess of existing MIL networks can be enhanced. Furthermore, deploying an ensemble of digital pathology foundation models yields higher baseline accuracy, although the benefits appear to diminish with more complex MIL architectures. Our code will be made publicly available upon acceptance.
- J. Van der Laak, G. Litjens, and F. Ciompi, “Deep learning in histopathology: the path to the clinic,” Nature Medicine, vol. 27, no. 5, pp. 775–784, 2021.
- T. Qaiser, C.-Y. Lee, M. Vandenberghe, J. Yeh, M. Gavrielides, J. Hipp, M. Scott, and J. Reischl, “Usability of deep learning and he images predict disease outcome-emerging tool to optimize clinical trials,” npj Precision Oncology, vol. 6, p. 37, 06 2022.
- X. Zhu, J. Yao, and J. Huang, “Deep convolutional neural network for survival analysis with pathological images,” in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), (China, Shenzhen), pp. 544–547, December 2016.
- X. Zhu, J. Yao, F. Zhu, and J. Huang, “Wsisa: Making survival prediction from whole slide histopathological images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Honolulu,USA), pp. 6855–6863, July 2017.
- B. Li, Y. Li, and K. W. Eliceiri, “Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14318–14328, 2021.
- M. Y. Lu, D. F. Williamson, T. Y. Chen, R. J. Chen, M. Barbieri, and F. Mahmood, “Data-efficient and weakly supervised computational pathology on whole-slide images,” Nature Biomedical Engineering, vol. 5, no. 6, pp. 555–570, 2021.
- Z. Shao, H. Bian, Y. Chen, Y. Wang, J. Zhang, X. Ji, and y. zhang, “Transmil: Transformer based correlated multiple instance learning for whole slide image classification,” in Advances in Neural Information Processing Systems (M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, eds.), vol. 34, pp. 2136–2147, Curran Associates, Inc., 2021.
- R. J. "Chen, T. Ding, M. Y. Lu, D. F. K. Williamson, G. Jaume, A. H. Song, B. Chen, A. Zhang, D. Shao, M. Shaban, M. Williams, L. Oldenburg, L. L. Weishaupt, J. J. Wang, A. Vaidya, L. P. Le, G. Gerber, S. Sahai, W. Williams, and F. Mahmood, “"towards a general-purpose foundation model for computational pathology",” "Nature Medicine", vol. 30, pp. 850–862, mar 2024.
- M. Ilse, J. Tomczak, and M. Welling, “Attention-based deep multiple instance learning,” in Proceedings of the 35th International Conference on Machine Learning (J. Dy and A. Krause, eds.), vol. 80 of Proceedings of Machine Learning Research, pp. 2127–2136, PMLR, 10–15 Jul 2018.
- G. Liu, J. Wu, and Z.-H. Zhou, “Key instance detection in multi-instance learning,” in Asian conference on machine learning, pp. 253–268, PMLR, 2012.
- X. Wang, Y. Yan, P. Tang, X. Bai, and W. Liu, “Revisiting multiple instance neural networks,” Pattern Recognition, vol. 74, pp. 15–24, 2018.
- M. U. Oner, J. M. S. Kye-Jet, H. K. Lee, and W.-K. Sung, “Studying the effect of mil pooling filters on mil tasks,” 2020.
- L. Fillioux, J. Boyd, M. Vakalopoulou, P.-h. Cournède, and S. Christodoulidis, “Structured state space models for multiple instance learning in digital pathology,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, and R. Taylor, eds.), (Cham), pp. 594–604, Springer Nature Switzerland, 2023.
- S. Yang, Y. Wang, and H. Chen, “Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology,” 2024.
- H. Steck, B. Krishnapuram, C. Dehing-oberije, P. Lambin, and V. C. Raykar, “On ranking in survival analysis: Bounds on the concordance index,” in Advances in Neural Information Processing Systems, vol. 20, (Vancouver, Canada), p. 1209–1216, December 2007.
- K. M. Papadopoulos, P. Barmpoutis, T. Stathaki, V. Kepenekian, P. Dartigues, S. Valmary-Degano, C. Illac-Vauquelin, G. Avérous, A. Chevallier, M.-H. Laverriere, L. Villeneuve, O. Glehen, S. Isaac, J. Hommell-Fontaine, F. Ng Kee Kwong, and N. Benzerdjeb, “Overall survival time estimation for epithelioid peritoneal mesothelioma patients from whole-slide images,” BioMedInformatics, vol. 4, no. 1, pp. 823–836, 2024.
- Z. Wang, Q. Gao, X. Yi, X. Zhang, Y. Zhang, D. Zhang, P. Liò, C. Bain, R. Bassed, S. Li, Y. Guo, S. Imoto, J. Yao, R. J. Daly, and J. Song, “Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images,” Computer Methods and Programs in Biomedicine, vol. 241, p. 107733, 2023.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.