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Foundation models have exhibited remarkable success in various applications, such as disease diagnosis and text report generation.
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics 43
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Mesejo, P., Pizarro, D., Abergel, A., Rouquette, O., Beorchia, S., Poincloux, L., Bartoli, A.: Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE TMI 35
2016
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Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: ICLR (2017)
2017
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
2019
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Borgli, H., Thambawita, V., Smedsrud, P.H., Hicks, S., Jha, D., Eskeland, S.L., Randel, K.R., Pogorelov, K., Lux, M., Nguyen, D.T.D., et al.: Hyperkvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific data 7
2020
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Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML. vol. 2, p. 4 (2021)
2021
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)
2021
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
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Hu, K., Shao, J., Liu, Y., Raj, B., Savvides, M., Shen, Z.: Contrast and order representations for video self-supervised learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7939–7949 (2021)
2021
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Li, K., Fathan, M.I., Patel, K., Zhang, T., Zhong, C., Bansal, A., Rastogi, A., Wang, J.S., Wang, G.: Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations. Plos one 16
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Ma, Y., Chen, X., Cheng, K., Li, Y., Sun, B.: Ldpolypvideo benchmark: A large-scale colonoscopy video dataset of diverse polyps. In: MICCAI. Springer (2021)
2021
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Misawa, M., Kudo, S.e., Mori, Y., Hotta, K., Ohtsuka, K., Matsuda, T., Saito, S., Kudo, T., Baba, T., Ishida, F., et al.: Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointestinal endoscopy 93
2021
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Naseer, M.M., Ranasinghe, K., Khan, S.H., Hayat, M., Shahbaz Khan, F., Yang, M.H.: Intriguing properties of vision transformers. NeurIPS (2021)
2021
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Qian, R., Meng, T., Gong, B., Yang, M.H., Wang, H., Belongie, S., Cui, Y.: Spatiotemporal contrastive video representation learning. In: CVPR (2021)
2021
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Nwoye, C.I., Yu, T., Gonzalez, C., Seeliger, B., Mascagni, P., Mutter, D., Marescaux, J., Padoy, N.: Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos. MedIA 78
2022
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Pan, J., Lin, Z., Zhu, X., Shao, J., Li, H.: St-adapter: Parameter-efficient image-to-video transfer learning for action recognition. NeurIPS (2022)
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Park, J., Lee, J., Kim, I.J., Sohn, K.: Probabilistic representations for video contrastive learning. In: CVPR. pp. 14711–14721 (2022)
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Qian, R., Ding, S., Liu, X., Lin, D.: Static and dynamic concepts for self-supervised video representation learning. In: ECCV. pp. 145–164. Springer (2022)
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Ranasinghe, K., Naseer, M., Khan, S., Khan, F.S., Ryoo, M.: Self-supervised video transformer. In: CVPR (June 2022)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. pp. 8748–8763. PMLR (2021)
2021
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Smedsrud, P.H., Thambawita, V., Hicks, S.A., Gjestang, H., Nedrejord, O.O., Næss, E., Borgli, H., Jha, D., Berstad, T.J.D., Eskeland, S.L., et al.: Kvasir-capsule, a video capsule endoscopy dataset. Scientific Data 8
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2021
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Boecking, B., Usuyama, N., Bannur, S., Castro, D.C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al.: Making the most of text semantics to improve biomedical vision–language processing. In: ECCV. pp. 1–21. Springer (2022)
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Ding, S., Li, M., Yang, T., Qian, R., Xu, H., Chen, Q., Wang, J., Xiong, H.: Motion-aware contrastive video representation learning via foreground-background merging. In: CVPR. pp. 9716–9726 (2022)
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Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: BrainLes, MICCAI. pp. 272–284. Springer (2022)
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Ji, G.P., Xiao, G., Chou, Y.C., Fan, D.P., Zhao, K., Chen, G., Van Gool, L.: Video polyp segmentation: A deep learning perspective. Machine Intelligence Research 19
2022
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Moon, J.H., Lee, H., Shin, W., Kim, Y.H., Choi, E.: Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE JBHI 26
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2022
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Tang, Y., Yang, D., Li, W., Roth, H.R., Landman, B., Xu, D., Nath, V., Hatamizadeh, A.: Self-supervised pre-training of swin transformers for 3d medical image analysis. In: CVPR. pp. 20730–20740 (2022)
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Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., Verjans, J., Carneiro, G.: Contrastive transformer-based multiple instance learning for weakly supervised polyp frame detection. In: MICCAI. pp. 88–98. Springer (2022)
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Willemink, M.J., Roth, H.R., Sandfort, V.: Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiology: Artificial Intelligence 4
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Fu, Z., Jiao, J., Yasrab, R., Drukker, L., Papageorghiou, A.T., Noble, J.A.: Anatomy-aware contrastive representation learning for fetal ultrasound. In: ECCV Workshops. pp. 422–436. Springer (2023)
2023
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2023
Closest in time.
Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., Rajpurkar, P.: Foundation models for generalist medical artificial intelligence. Nature 616
2023
Closest in time.