11/6/2023 0 Comments Image mixer 1.1![]() Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Advances in Neural Information Processing Systems, vol. Tolstikhin, I.O., et al.: MLP-Mixer: an all-MLP architecture for vision. ![]() 933–941 (2022)ĭosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. Hou, W., et al.: H^ 2-MIL: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. Hashimoto, N., et al.: Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images. (eds.) Medical Image Computing and Computer Assisted Intervention. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. Yang, J., et al.: ReMix: a general and efficient framework for multiple instance learning based whole slide image classification. Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Wang, Z., Yu, L., Ding, X., Liao, X., Wang, L.: Lymph node metastasis prediction from whole slide images with transformer-guided multiinstance learning and knowledge transfer. Huang, Z., Chai, H., Wang, R., Wang, H., Yang, Y., Wu, H.: Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images. Li, H., et al.: DT-MIL: deformable transformer for multi-instance learning on histopathological image. Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. 14318–14328 (2021)Ĭhen, R.J.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: International Conference on Machine Learning, pp. Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Zheng, Y., et al.: A graph-transformer for whole slide image classification. Javed, S., et al.: Cellular community detection for tissue phenotyping in colorectal cancer histology images. Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Zarella, M.D., et al.: A practical guide to whole slide imaging: a white paper from the digital pathology association. ![]() 25(8), 1301–1309 (2019)īera, K., Schalper, K.A., Rimm, D.L., Velcheti, V., Madabhushi, A.: Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. KeywordsĬampanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. The experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications. Thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information of WSI. Whole slide image (WSI) classification is an essential task in computational pathology.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |