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Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks.
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Chan, L.W., Fung, T.Y., Leung, T.Y., Sahota, D.S., Lau, T.K.: Volumetric (3d) imaging reduces inter- and intraobserver variation of fetal biometry measurements. Ultrasound in Obstetrics & Gynecology 33
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Salomon, L.J., Alfirevic, Z., Berghella, V., Bilardo, C., Hernandez-Andrade, E., Johnsen, S.L., Kalache, K., Leung, K.Y., Malinger, G., Munoz, H., Prefumo, F., Toi, A., Lee, W., Committee, I.C.S.: Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound in Obstetrics & Gynecology 37
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Sarris, I., Ioannou, C., Chamberlain, P., Ohuma, E., Roseman, F., Hoch, L., Altman, D.G., Papageorghiou, A.T., Fetal, I., Century (INTERGROWTH‐21st), N.G.C.: Intra- and interobserver variability in fetal ultrasound measurements. Ultrasound in Obstetrics & Gynecology 39
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Salomon, L.J., Alfirevic, Z., Bilardo, C.M., Chalouhi, G.E., Ghi, T., Kagan, K.O., Lau, T.K., Papageorghiou, A.T., Raine-Fenning, N.J., Stirnemann, J., Suresh, S., Tabor, A., Timor-Tritsch, I.E., Toi, A., Yeo, G.: ISUOG practice guidelines: performance of first-trimester fetal ultrasound scan. Ultrasound in Obstetrics & Gynecology 41
2013
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Maraci, M.A., Napolitano, R., Papageorghiou, A., Noble, J.A.: Searching for structures of interest in an ultrasound video sequence. In: Wu, G., Zhang, D., Zhou, L. (eds.) Machine Learning in Medical Imaging, pp. 133–140. Springer, Cham (2014)
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Whitworth, M., Bricker, L., Mullan, C.: Ultrasound for fetal assessment in early pregnancy. Cochrane Database of Systematic Reviews (7) (2015) https://doi.org/10.1002/14651858.CD007058.pub3
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Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Erk, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1715–1725. Association for Computational Linguistics, Berlin, Germany (2016). https://doi.org/10.18653/v1/P16-1162 . https://aclanthology.org/P16-1162
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Wanyonyi, S.Z., Mariara, C.M., Vinayak, S., Stones, W.: Opportunities and challenges in realizing universal access to obstetric ultrasound in sub-saharan africa. Ultrasound International Open 3
2017
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Baumgartner, C.F., Kamnitsas, K., Matthew, J., Fletcher, T.P., Smith, S., Koch, L.M., Kainz, B., Rueckert, D.: Sononet: Real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Transactions on Medical Imaging 36
2017
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Kiserud, T., Piaggio, G., Carroli, G., Widmer, M., Carvalho, J., Neerup Jensen, L., Giordano, D., Cecatti, J.G., Abdel Aleem, H., Talegawkar, S.A., Benachi, A., Diemert, A., Tshefu Kitoto, A., Thinkhamrop, J., Lumbiganon, P., Tabor, A., Kriplani, A., Gonzalez Perez, R., Hecher, K., Hanson, M.A., Gülmezoglu, A.M., Platt, L.D.: The world health organization fetal growth charts: A multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight. PLoS Med 14
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Heuvel, T.L.A., Bruijn, D., Korte, C.L., Ginneken, B.: Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One 13
2018
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)
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Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., Gratacós, E.: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Scientific Reports 10
2020
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Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., Hu, X.: Score-cam: Score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020)
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Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: Fast and flexible image augmentations. Information 11
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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., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (2021). https://api.semanticscholar.org/CorpusID:231591445
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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. ICLR (2021)
2021
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Northcutt, C., Jiang, L., Chuang, I.: Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research 70
2021
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Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., Schmidt, L.: OpenCLIP. https://doi.org/10.5281/zenodo.5143773 . If you use this software, please cite it as below. https://doi.org/10.5281/zenodo.5143773
Silva-Rodríguez, J., Chakor, H., Kobbi, R., Dolz, J., Ben Ayed, I.: A foundation language-image model of the retina (flair): encoding expert knowledge in text supervision. Medical Image Analysis 99
2024
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2024
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Lu, M.Y., Chen, B., Williamson, D.F.K., Chen, R.J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L.P., Gerber, G., Parwani, A.V., Zhang, A., Mahmood, F.: A visual-language foundation model for computational pathology. Nature Medicine 30
2024
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Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F.K., Jaume, G., Song, A.H., Chen, B., Zhang, A., Shao, D., Shaban, M., Williams, M., Oldenburg, L., Weishaupt, L.L., Wang, J.J., Vaidya, A., Le, L.P., Gerber, G., Sahai, S., Williams, W., Mahmood, F.: Towards a general-purpose foundation model for computational pathology. Nature Medicine 30
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2021
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Wang, Z., Wu, Z., Agarwal, D., Sun, J.: MedCLIP: Contrastive learning from unpaired medical images and text. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3876–3887. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https://doi.org/10.18653/v1/2022.emnlp-main.256 . https://aclanthology.org/2022.emnlp-main.256/
2022
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Abuhamad, A.Z., Chaoui, R.: A Practical Guide to Fetal Echocardiography Normal and Abnormal Hearts. Wolters Kluwer (2022)
2022
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Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1748–1758 (2022). https://doi.org/10.1109/WACV51458.2022.00181
2022
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T.J., Zou, J.: A visual–language foundation model for pathology image analysis using medical twitter. Nature Medicine 29
2023
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Slimani, S., Hounka, S., Mahmoudi, A., Rehah, T., Laoudiyi, D., Saadi, H., Bouziyane, A., Lamrissi, A., Jalal, M., Bouhya, S., Akiki, M., Bouyakhf, Y., Badaoui, B., Radgui, A., Mhlanga, M., Bouyakhf, E.H.: Fetal biometry and amniotic fluid volume assessment end-to-end automation using deep learning. Nature Communications 14
2023
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Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., Jitsev, J.: Reproducible scaling laws for contrastive language-image learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2818–2829 (2023)
2023
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2024
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Christensen, M., Vukadinovic, M., Yuan, N., Ouyang, D.: Vision–language foundation model for echocardiogram interpretation. Nature Medicine 30
2024
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2024
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2024
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2024
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Lu, M.Y., Chen, B., Williamson, D.F.K., Chen, R.J., Zhao, M., Chow, A.K., Ikemura, K., Kim, A., Pouli, D., Patel, A., Soliman, A., Chen, C., Ding, T., Wang, J.J., Gerber, G., Liang, I., Le, L.P., Parwani, A.V., Weishaupt, L.L., Mahmood, F.: A multimodal generative ai copilot for human pathology. Nature 634
2024
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Zhang, J., Wang, K., Xu, R., Zhou, G., Hong, Y., Fang, X., Wu, Q., Zhang, Z., Wang, H.: Navid: Video-based vlm plans the next step for vision-and-language navigation. Robotics: Science and Systems (2024)
2024
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2024
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Rasheed, H., Maaz, M., Shaji, S., Shaker, A., Khan, S., Cholakkal, H., Anwer, R.M., Xing, E., Yang, M.-H., Khan, F.S.: Glamm: Pixel grounding large multimodal model. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024)
2024
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Healy, J., McInnes, L.: Uniform manifold approximation and projection. Nature Reviews Methods Primers 4
2024
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Alasmawi, H., Bricker, L., Yaqub, M.: Fusc: Fetal ultrasound semantic clustering of second-trimester scans using deep self-supervised learning. Ultrasound in Medicine & Biology 50
2024
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Liang, X., Li, X., Li, F., Jiang, J., Dong, Q., Wang, W., Wang, K., Dong, S., Luo, G., Li, S.: Medfilip: Medical fine-grained language-image pre-training. IEEE Journal of Biomedical and Health Informatics (2025)
2025
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2025
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JaidedAI: EasyOCR. https://github.com/JaidedAI/EasyOCR (2025)
2025
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