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Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities.
Language models are few-shot learners
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Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels
Hoover, A., Goldbaum, M., 2003 · 2003
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Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales
Wilkinson, C.P., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T., Lum, F., 2003 · 2003
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The unified medical language system (umls): Integrating biomedical terminology
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Hamel, C., 2006 · 2006
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The diaretdb1 diabetic retinopathy database and evaluation protocol, in: Proceedings of the British Machine Vision Conference (BMVC), pp. 1–18
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Identification of the optic nerve head with genetic algorithms
Carmona, E.J., Rincón, M., García-Feijoó, J., de-la Casa, J.M.M., 2008 · 2008
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Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators
Farnell, D.J., Hatfield, F.N., Knox, P., Reakes, M., Spencer, S., Parry, D., Harding, S.P., 2008 · 2008
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Imagenet: A large-scale hierarchical image database, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
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The difficulty of training deep architectures and the effect of unsupervised pre-training, in: Proceedings of the International Conference on Artificial Intelligence and Statistics (PMLR), pp. 153–160
Erhan, D., Manzagol, P.A., Bengio, Y., Bengio, S., Vincent, P., 2009 · 2009
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Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs
Niemeijer, M., Ginneken, B.V., Cree, M.J., Mizutani, A., Quellec, G., Sanchez, C.I., Zhang, B., Hornero, R., Lamard, M., Muramatsu, C., Wu, X., Cazuguel, G., You, J., Mayo, A., Li, Q., Hatanaka, Y., Cochener, B., Roux, C., Karray, F., Garcia, M., Fujita, H., Abramoff, M.D., 2010 · 2010
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Origa-light: An online retinal fundus image database for glaucoma analysis and research, in: Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 3065–3068
Zhang, Z., Yin, F.S., Liu, J., Wong, W.K., Tan, N.M., Lee, B.H., Cheng, J., Wong, T.Y., 2010 · 2010
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Exudate-based diabetic macular edema detection in fundus images using publicly available datasets
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Garg, S., Tobin, K.W., Chaum, E., 2012 · 2012
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Robust vessel segmentation in fundus images
Budai, A., Bock, R., Maier, A., Hornegger, J., Michelson, G., 2013 · 2013
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Teleophta: Machine learning and image processing methods for teleophthalmology
Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.C., Meyer, F., Marcotegui, B., Quellec, G., Lamard, M., Danno, R., Elie, D., Massin, P., Viktor, Z., Erginay, A., Laÿ, B., Chabouis, A., 2013 · 2013
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Feedback on a publicly distributed image database: The messidor database
Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordóñez-Varela, J.R., Massin, P., Erginay, A., Charton, B., Klein, J.C., 2014 · 2014
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Advancing bag-of-visual-words representations for lesion classification in retinal images
Pires, R., Jelinek, H.F., Wainer, J., Valle, E., Rocha, A., 2014 · 2014
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Drishti-gs retinal image dataset for optic nerve head segmentation, in: International Symposium on Biomedical Imaging (ISBI), pp. 53–56
Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., Jain, M., Tabish, A.U.S., 2014 · 2014
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Diabetic Retinopathy Detection Challenge
Kaggle, 2015 · 2015
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Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning
Abramoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., Niemeijer, M., 2016 · 2016
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Deep residual learning for image recognition, in: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–12
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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Exploiting ensemble learning for automatic cataract detection and grading
Yang, J.J., Li, J., Shen, R., Zeng, Y., He, J., Bi, J., Li, Y., Zhang, Q., Peng, L., Wang, Q., 2016 · 2016
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J., 2017 · 2017
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Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., Kawashima, H., 2017 · 2017
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Fauw, J.D., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., van den Driessche, G., Lakshminarayanan, B., Meyer, C., Mackinder, F., Bouton, S., Ayoub, K., Chopra, R., King, D., Karthikesalingam, A., Hughes, C.O., Raine, R., Hughes, J., Sim, D.A., Egan, C., Tufail, A., Montgomery, H., Hassabis, D., Rees, G., Back, T., Khaw, P.T., Suleyman, M., Cornebise, J., Keane, P.A., Ronneberger, O., 2018 · 2018
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Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G.S., Peng, L., Webster, D.R., 2018 · 2018
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Radiology objects in context (roco): A multimodal image dataset, in: MICCAI Workshop: Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS), p. 180–189
Pelka, O., Koitka, S., Rückert, J., Nensa, F., Friedrich, C.M., 2018 · 2018
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Laser scar detection in fundus images using convolutional neural networks, in: Asian Conference on Computer Vision (ACCV), pp. 191–206
Wei, Q., Li, X., Wang, H., Ding, D., Yu, W., Chen, Y., 2018 · 2018
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Publicly available clinical BERT embeddings, in: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72––78
Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jin, D., Naumann, T., McDermott, M., 2019 · 2019
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Promising artificial intelligence–machine learning–deep learning algorithms in ophthalmology
Balyen, L., Peto, T., 2019 · 2019
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Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in africa: a clinical validation study
Bellemo, V., Lim, Z.W., Lim, G., Nguyen, Q.D., Xie, Y., Yip, M.Y., Hamzah, H., Ho, J., Lee, X.Q., Hsu, W., Lee, M.L., Musonda, L., Chandran, M., Chipalo-Mutati, G., Muma, M., Tan, G.S., Sivaprasad, S., Menon, G., Wong, T.Y., Ting, D.S., 2019 · 2019
Cited alongside, same era.
Padchest: A large chest x-ray image dataset with multi-label annotated reports
Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M., 2019 · 2019
Cited alongside, same era.
Cnns for automatic glaucoma assessment using fundus images: An extensive validation
Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J.M., Navea, A., 2019 · 2019
Cited alongside, same era.
Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal sd-oct scans
Hassan, T., Akram, M.U., Masood, M.F., Yasin, U., 2019 · 2019
Cited alongside, same era.
Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
A survey on incorporating domain knowledge into deep learning for medical image analysis
Xie, X., Niu, J., Member, S., Liu, X., Chen, Z., Tang, S., Yu, S., 2021 · 2021
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Rethinking Supervised Pre-Training for Better Downstream Transferring, in: International Conference on Learning Representations (ICLR), pp. 1–22
Feng, Y., Jiang, J., Tang, M., Jin, R., Gao, Y., 2022 · 2022
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Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15979–15988
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
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Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9068–9077
Hu, S.X., Li, D., Stühmer, J., Kim, M., Hospedales, T.M., 2022 · 2022
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Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., ying Deng, C., Mark, R.G., Horng, S., 2019 · 2019
Cited alongside, same era.
APTOS 2019 Blindness Detection
Karthik, M., Dane, S., 2019 · 2019
Cited alongside, same era.
Competition on Ocular Disease Intelligent Recognition
NIHDS-PKU, 2019 · 2019
Cited alongside, same era.
Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Orlando, J.I., Fu, H., Breda, J.B., van Keer, K., Bathula, D.R., Diaz-Pinto, A., Fang, R., Heng, P.A., Kim, J., Lee, J., Lee, J., Li, X., Liu, P., Lu, S., Murugesan, B., Naranjo, V., Phaye, S.S.R., Shankaranarayana, S.M., Sikka, A., Son, J., van den Hengel, A., Wang, S., Wu, J., Wu, Z., Xu, G., Xu, Y., Yin, P., Li, F., Zhang, X., Xu, Y., Zhang, X., Bogunović, H., 2019 · 2019
Cited alongside, same era.
Transfusion: Understanding transfer learning for medical imaging, in: Advances in neural information processing systems (NeurIPS), pp. 1–11
Raghu, M., Zhang, C., Kleinberg, J., Bengio, S., 2019 · 2019
Cited alongside, same era.
Myopic maculopathy: Current status and proposal for a new classification and grading system (atn)
Ruiz-Medrano, J., Montero, J.A., Flores-Moreno, I., Arias, L., García-Layana, A., Ruiz-Moreno, J.M., 2019 · 2019
Cited alongside, same era.
World report of vision
WHO, 2019 · 2019
Cited alongside, same era.
Bira-net bilinear attention net for diabetic retinopathy grading, in: International Conference on Image Processing (ICIP), pp. 1385–1389
Zhao, Z., Zhang, K., Hao, X., Tian, J., Chua, M.C.H., Chen, L., Xu, X., 2019 · 2019
Cited alongside, same era.
Fives: A fundus image dataset for artificial intelligence based vessel segmentation
Jin, K., Huang, X., Zhou, J., Li, Y., Yan, Y., Sun, Y., Zhang, Q., Wang, Y., Ye, J., 2022 · 2022
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Papila: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment
Kovalyk, O., Morales-Sánchez, J., Verdú-Monedero, R., Sellés-Navarro, I., Palazón-Cabanes, A., Sancho-Gómez, J.L., 2022 · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution, in: International Conference on Learning Representations (ICLR), pp. 1–42
Kumar, A., Raghunathan, A., Jones, R.M., Ma, T., Liang, P., 2022 · 2022
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Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge
Liu, R., Wang, X., Wu, Q., Dai, L., Fang, X., Yan, T., Son, J., Tang, S., Li, J., Gao, Z., Galdran, A., Poorneshwaran, J.M., Liu, H., Wang, J., Chen, Y., Porwal, P., Tan, G.S.W., Yang, X., Dai, C., Song, H., Chen, M., Li, H., Jia, W., Shen, D., Sheng, B., Zhang, P., 2022 · 2022
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What makes transfer learning work for medical images: Feature reuse and other factors, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9225–9234
Matsoukas, C., Haslum, J.F., Sorkhei, M., Söderberg, M., Smith, K., 2022 · 2022
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Joint learning of localized representations from medical images and reports, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 1–17
Müller, P., Kaissis, G., Zou, C., Rueckert, D., 2022 · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
Shu, M., Nie, W., Huang, D.A., Yu, Z., Goldstein, T., Anandkumar, A., Xiao, C., 2022 · 2022
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Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning
Tiu, E., Talius, E., Patel, P., Langlotz, C.P., Ng, A.Y., Rajpurkar, P., 2022 · 2022
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Robust fine-tuning of zero-shot models, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7959–7971
Wortsman, M., Ilharco, G., Kim, J.W., Li, M., Kornblith, S., Roelofs, R., Gontijo-Lopes, R., Hajishirzi, H., Farhadi, A., Namkoong, H., Schmidt, L., 2022 · 2022
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Foundation models in healthcare: Opportunities, biases and regulatory prospects in europe
Wójcik, M.A., 2022 · 2022
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Unified contrastive learning in image-text-label space, in: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19163–19173
Yang, J., Li, C., Zhang, P., Xiao, B., Liu, C., Yuan, L., Gao, J., 2022 · 2022
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Zhao, S., Zhang, Z., Schulter, S., Zhao, L., Vijay Kumar, B., Stathopoulos, A., Chandraker, M., Metaxas, D.N., 2022 · 2022
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Robust and efficient medical imaging with self-supervision
Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., MacWilliams, P., Mahdavi, S.S., Wulczyn, E., et al., 2023 · 2023
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Pubmedclip: How much does clip benefit visual question answering in the medical domain?, in: Findings of the Association for Computational Linguistics: EACL 2023, pp. 1151–1163
Eslami, S., Meinel, C., De Melo, G., 2023 · 2023
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Clip-adapter: Better vision-language models with feature adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., Qiao, Y., 2023 · 2023
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Finetune like you pretrain: Improved finetuning of zero-shot vision models, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19338–19347
Goyal, S., Kumar, A., Garg, S., Raghunathan, Z.K.A., 2023 · 2023
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Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Huang, S.C., Pareek, A., Jensen, M., Lungren, M.P., Yeung, S., Chaudhari, A.S., 2023 · 2023
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Chaksu: A glaucoma specific fundus image database
Kumar, J.R., Seelamantula, C.S., Gagan, J.H., Kamath, Y.S., Kuzhuppilly, N.I., Vivekanand, U., Gupta, P., Patil, S., 2023 · 2023
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Clip-driven universal model for organ segmentation and tumor detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1–23
Liu, J., Zhang, Y., Chen, J.N., Xiao, J., Lu, Y., Landman, B.A., Yuan, Y., Yuille, A., Tang, Y., Zhou, Z., 2023 · 2023
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Visual language pretrained multiple instance zero-shot transfer for histopathology images, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19764–19775
Lu, M.Y., Chen, B., Zhang, A., Williamson, D.F.K., Chen, R.J., Ding, T., Le, L.P., Chuang, Y.S., Mahmood, F., 2023 · 2023
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Visual classification via description from large language models, in: International Conference of Learning Representations (ICLR), pp. 1–17
Menon, S., Vondrick, C., 2023 · 2023
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Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., Rajpurkar, P., 2023 · 2023
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A brazilian multilabel ophthalmological dataset (brset), in: PhysioNet, p. 1
Nakayama, L.F., Goncalves, M., Zago Ribeiro, L., Santos, H., Ferraz, D., Malerbi, F., Celi, L.A., Regatieri, C., 2023 · 2023
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Medical image understanding with pretrained vision language models: a comprehensive study, in: International Conference on Learing Representations (ICLR), pp. 1–20
Qin, Z., Yi, H., Lao, Q., Li, K., 2023 · 2023
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No reason for no supervision: Improved generalization in supervised models, in: International Conference on Learning Representations (ICLR), pp. 1–27
Sariyildiz, M.B., Kalantidis, Y., Alahari, K., Larlus, D., 2023 · 2023
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Generalization of vision pre-trained models for histopathology
Sikaroudi, M., Hosseini, M., Gonzalez, R., Rahnamayan, S., Tizhoosh, H.R., 2023 · 2023
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Vision-language modelling for radiological imaging and reports in the low data regime, in: Medical Image with Deep Learning (MIDL), pp. 1–21
Windsor, R., Jamaludin, A., Kadir, T., Zisserman, A., 2023 · 2023
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Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 21372–21383
Wu, C., Zhang, X., Zhang, Y., Wang, Y., Xie, W., 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., et al., 2023 · 2023
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A competition for the diagnosis of myopic maculopathy by artificial intelligence algorithms
Qian, B., Sheng, B., Chen, H., Wang, X., Li, T., Jin, Y., Guan, Z., Jiang, Z., Wu, Y., Wang, J., et al., 2024 · 2024
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Airogs: Artificial intelligence for robust glaucoma screening challenge
de Vente, C., Vermeer, K.A., Jaccard, N., Wang, H., Sun, H., Khader, F., Truhn, D., Aimyshev, T., Zhanibekuly, Y., Le, T.D., Galdran, A., Gonzalez Ballester, M.A., Carneiro, G., G, D.R., S, H.P., Puthussery, D., Liu, H., Yang, Z., Kondo, S., Kasai, S., Wang, E., Durvasula, A., Heras, J., Zapata, M.A., Araujo, T., Aresta, G., Bogunovic, H., Arikan, M., Lee, Y.C., Cho, H.B., Choi, Y.H., Qayyum, A., Razzak, I., van Ginneken, B., Lemij, H.G., Sanchez, C.I., 2024 · 2024
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A multimodal biomedical foundation model trained from fifteen million image–text pairs
Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., Wong, C., Tupini, A., Wang, Y., Mazzola, M., Shukla, S., Liden, L., Gao, J., Crabtree, A., Piening, B., Bifulco, C., Lungren, M.P., Naumann, T., Wang, S., Poon, H., 2024 · 2024
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