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Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods.
Imagenet: A large-scale hierarchical image database, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 248–255
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
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Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al., 2017 · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE international conference on computer vision (ICCV), pp. 2223–2232
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., Komodakis, N., 2018 · 2018
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100,000 histological images of human colorectal cancer and healthy tissue
Kather, J.N., Halama, N., Marx, A., 2018 · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al., 2020 · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Jing, L., Tian, Y., 2020 · 2020
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Self-supervised nuclei segmentation in histopathological images using attention, in: Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer. pp. 393–402
Sahasrabudhe, M., Christodoulidis, S., Salgado, R., Michiels, S., Loi, S., André, F., Paragios, N., Vakalopoulou, M., 2020 · 2020
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021 · 2021
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Exploring simple siamese representation learning, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 15750–15758
Chen, X., He, K., 2021 · 2021
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An empirical study of training self-supervised vision transformers, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 9640–9649
Chen, X., Xie, S., He, K., 2021 · 2021
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Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images, in: Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer. pp. 561–570
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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 · 2021
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Restainnet: a self-supervised digital re-stainer for stain normalization
Zhao, B., Han, C., Pan, X., Lin, J., Yi, Z., Liang, C., Chen, X., Li, B., Qiu, W., Li, D., et al., 2022 · 2022
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Context-aware self-supervised learning of whole slide images
Aryal, M., Yahyasoltani, N., 2023 · 2023
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A general-purpose self-supervised model for computational pathology
Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F.K., Jaume, G., Chen, B., Zhang, A., Shao, D., Song, A.H., Shaban, M., Williams, M., Vaidya, A., Sahai, S., Oldenburg, L., Weishaupt, L.L., Wang, J.J., Williams, W., Le, L.P., Gerber, G., Mahmood, F., 2023 · 2023
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Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks
Kawai, M., Ota, N., Yamaoka, S., 2023 · 2023
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Hyreco-hybrid re-stained and consecutive histological serial sections
van der Laak, J., Lotz, J., Weiss, N., Heldmann, S., 2021 · 2021
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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 (CVPR), pp. 14318–14328
Li, B., Li, Y., Eliceiri, K.W., 2021 · 2021
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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., Mahmood, F., 2021 · 2021
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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 · 2021
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Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection
Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B., Thiran, J.P., 2022 · 2022
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Self supervised contrastive learning for digital histopathology
Ciga, O., Xu, T., Martel, A.L., 2022 · 2022
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Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 16000–16009
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
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Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1815–1824
Liu, S., Zhu, C., Xu, F., Jia, X., Shi, Z., Jin, M., 2022 · 2022
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Giga-ssl: Self-supervised learning for gigapixel images, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4304–4313
Lazard, T., Lerousseau, M., Decencière, E., Walter, T., 2023 · 2023
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D-lmbmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry
Li, Z., Shang, Z., Liu, J., Zhen, H., Zhu, E., Zhong, S., Sturgess, R.N., Zhou, Y., Hu, X., Zhao, X., Wu, Y., Li, P., Lin, R., Ren, J., 2023 · 2023
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Self-supervised digital histopathology image disentanglement for arbitrary domain stain transfer
Ling, Y., Tan, W., Yan, B., 2023 · 2023
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Towards 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., Zhang, A., Le, L.P., et al., 2023 · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P., 2023 · 2023
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Shekhar, S., Bordes, F., Vincent, P., Morcos, A., 2023 · 2023
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Virchow: A million-slide digital pathology foundation model
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Liu, S., Mathieu, P., van Eck, A., Lee, D., Viret, J., Robert, E., Wang, Y.K., Kunz, J.D., Lee, M.C.H., Bernhard, J., Godrich, R.A., Oakley, G., Millar, E., Hanna, M., Retamero, J., Moye, W.A., Yousfi, R., Kanan, C., Klimstra, D., Rothrock, B., Fuchs, T.J., 2023 · 2023
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Handcrafted histological transformer (h2t): Unsupervised representation of whole slide images
Vu, Q.D., Rajpoot, K., Raza, S.E.A., Rajpoot, N., 2023 · 2023
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A foundation model for generalizable disease detection from retinal images
Zhou, Y., Chia, M.A., Wagner, S.K., Ayhan, M.S., Williamson, D.J., Struyven, R.R., Liu, T., Xu, M., Lozano, M.G., Woodward-Court, P., Kihara, Y., Allen, N., Gallacher, J.E.J., Littlejohns, T., Aslam, T., Bishop, P., Black, G., Sergouniotis, P., Atan, D., Dick, A.D., Williams, C., Barman, S., Barrett, J.H., Mackie, S., Braithwaite, T., Carare, R.O., Ennis, S., Gibson, J., Lotery, A.J., Self, J., Chakravarthy, U., Hogg, R.E., Paterson, E., Woodside, J., Peto, T., Mckay, G., Mcguinness, B., Foster, P.J., Balaskas, K., Khawaja, A.P., Pontikos, N., Rahi, J.S., Lascaratos, G., Patel, P.J., Chan, M., Chua, S.Y.L., Day, A., Desai, P., Egan, C., Fruttiger, M., Garway-Heath, D.F., Hardcastle, A., Khaw, S.P.T., Moore, T., Sivaprasad, S., Strouthidis, N., Thomas, D., Tufail, A., Viswanathan, A.C., Dhillon, B., Macgillivray, T., Sudlow, C., Vitart, V., Doney, A., Trucco, E., Guggeinheim, J.A., Morgan, J.E., Hammond, C.J., Williams, K., Hysi, P., Harding, S.P., Zheng, Y., Luben, R., Luthert, P., Sun, Z., McKibbin, M., O’Sullivan, E., Oram, R., Weedon, M., Owen, C.G., Rudnicka, A.R., Sattar, N., Steel, D., Stratton, I., Tapp, R., Yates, M.M., Petzold, A., Madhusudhan, S., Altmann, A., Lee, A.Y., Topol, E.J., Denniston, A.K., Alexander, D.C., Keane, P.A., 2023 · 2023
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Rudolfv: A foundation model by pathologists for pathologists
Dippel, J., Feulner, B., Winterhoff, T., Milbich, T., Tietz, S., Schallenberg, S., Dernbach, G., Kunft, A., Heinke, S., Eich, M.L., Ribbat-Idel, J., Krupar, R., Anders, P., Prenißl, N., Jurmeister, P., Horst, D., Ruff, L., Müller, K.R., Klauschen, F., Alber, M., 2024 · 2024
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Dynamic graph representation with knowledge-aware attention for histopathology whole slide image analysis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11323–11332
Li, J., Chen, Y., Chu, H., Sun, Q., Guan, T., Han, A., He, Y., 2024 · 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., Liang, L., Chan, R.C.K., Wang, J., Cheng, K.T., Chen, H., 2024 · 2024
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Foundation model for endoscopy video analysis via large-scale self-supervised pre-train
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On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., Metaxas, D., 2024 · 2024
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