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Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models.
Scispacy: fast and robust models for biomedical natural language processing
Neumann, M., King, D., Beltagy, I., Ammar, W., 2019 · 1902
Earlier work this paper cites.
Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., et al., 2019 · 1902
Earlier work this paper cites.
Med3d: Transfer learning for 3d medical image analysis
Chen, S., Ma, K., Zheng, Y., 2019 · 1904
Earlier work this paper cites.
Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., et al., 2019 · 1904
Earlier work this paper cites.
Radiological assessment of osteo-arthrosis
Kellgren, J., Lawrence, J., 1957 · 1957
Earlier work this paper cites.
Occam’s razor
Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K., 1987 · 1987
Earlier work this paper cites.
Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect
Austin, J., Romney, B., Goldsmith, L., 1992 · 1992
Earlier work this paper cites.
Artificial convolution neural network techniques and applications for lung nodule detection
Lo, S.C., Lou, S.L., Lin, J.S., Freedman, M.T., Chien, M.V., Mun, S.K., 1995 · 1995
Earlier work this paper cites.
Breast imaging reporting and data system (bi-rads)
Liberman, L., Menell, J.H., 2002 · 2002
Earlier work this paper cites.
Subtle lung nodules: influence of local anatomic variations on detection
Samei, E., Flynn, M.J., Peterson, E., Eyler, W.R., 2003 · 2003
Earlier work this paper cites.
The unified medical language system (umls): integrating biomedical terminology
Bodenreider, O., 2004 · 2004
Earlier work this paper cites.
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E., 2020 · 2006
Earlier work this paper cites.
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al., 2020 · 2006
Earlier work this paper cites.
The national alzheimer’s coordinating center (nacc) database: the uniform data set
Beekly, D.L., Ramos, E.M., Lee, W.W., Deitrich, W.D., Jacka, M.E., Wu, J., Hubbard, J.L., Koepsell, T.D., Morris, J.C., Kukull, W.A., et al., 2007 · 2007
Earlier work this paper cites.
Community-acquired pneumonia in elderly patients
Stupka, J.E., Mortensen, E.M., Anzueto, A., Restrepo, M.I., 2009 · 2009
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al., 2020 · 2010
Earlier work this paper cites.
Sparse coding and high-order correlations in fine-scale cortical networks
Ohiorhenuan, I.E., Mechler, F., Purpura, K.P., Schmid, A.M., Hu, Q., Victor, J.D., 2010 · 2010
Earlier work this paper cites.
The basics of brain development
Stiles, J., Jernigan, T.L., 2010 · 2010
Earlier work this paper cites.
Medicat: A dataset of medical images, captions, and textual references
Subramanian, S., Wang, L.L., Mehta, S., Bogin, B., van Zuylen, M., Parasa, S., Singh, S., Gardner, M., Hajishirzi, H., 2020 · 2010
Earlier work this paper cites.
The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al., 2011 · 2011
Earlier work this paper cites.
The radiology report—are we getting the message across?
Wallis, A., McCoubrie, P., 2011 · 2011
Earlier work this paper cites.
Automatic segmentation of neonatal images using convex optimization and coupled level sets
Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D., 2011 · 2011
Earlier work this paper cites.
Iterative multi-atlas-based multi-image segmentation with tree-based registration
Jia, H., Yap, P.T., Shen, D., 2012 · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O., 2013 · 2013
Earlier work this paper cites.
Feedback on a publicly distributed image database: the messidor database
Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., et al., 2014 · 2014
Earlier work this paper cites.
Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age
Li, G., Nie, J., Wang, L., Shi, F., Lyall, A.E., Lin, W., Gilmore, J.H., Shen, D., 2014 · 2014
Earlier work this paper cites.
Multilabel image classification via high-order label correlation driven active learning
Zhang, B., Wang, Y., Chen, F., 2014 · 2014
Earlier work this paper cites.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, in: Proceedings of the IEEE international conference on computer vision, pp. 1635–1643
Dai, J., He, K., Sun, J., 2015 · 2015
Earlier work this paper cites.
Local wavelet pattern: a new feature descriptor for image retrieval in medical ct databases
Dubey, S.R., Singh, S.K., Singh, R.K., 2015 · 2015
Earlier work this paper cites.
Diabetic retinopathy detection
Emma, D., Jared, Jorge, Will, C., 2015 · 2015
Earlier work this paper cites.
Magnetic resonance imaging in alzheimer’s disease neuroimaging initiative 2
Jack Jr, C.R., Barnes, J., Bernstein, M.A., Borowski, B.J., Brewer, J., Clegg, S., Dale, A.M., Carmichael, O., Ching, C., DeCarli, C., et al., 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Earlier work this paper cites.
What’s the point: Semantic segmentation with point supervision, in: European conference on computer vision, Springer. pp. 549–565
Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L., 2016 · 2016
Earlier work this paper cites.
High-order resting-state functional connectivity network for mci classification
Chen, X., Zhang, H., Gao, Y., Wee, C.Y., Li, G., Shen, D., Initiative, A.D.N., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Multi-class texture analysis in colorectal cancer histology
Kather, J.N., Weis, C.A., Bianconi, F., Melchers, S.M., Schad, L.R., Gaiser, T., Marx, A., Zöllner, F.G., 2016 · 2016
Earlier work this paper cites.
Seed, expand and constrain: Three principles for weakly-supervised image segmentation, in: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, Springer. pp. 695–711
Kolesnikov, A., Lampert, C.H., 2016 · 2016
Earlier work this paper cites.
Scribblesup: Scribble-supervised convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3159–3167
Lin, D., Dai, J., Jia, J., He, K., Sun, J., 2016 · 2016
Earlier work this paper cites.
Classification of clinically useful sentences in clinical evidence resources
Morid, M.A., Fiszman, M., Raja, K., Jonnalagadda, S.R., Del Fiol, G., 2016 · 2016
Earlier work this paper cites.
Tintinalli’s Emergency Medicine: A Comprehensive Study Guide, 8e
Tintinalli, J.E., Stapczynski, J.S., Ma, O., Yealy, D., Meckler, G., Cline, D., 2016 · 2016
Earlier work this paper cites.
Latent embeddings for zero-shot classification, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 69–77
Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., Schiele, B., 2016 · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921–2929
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., 2016 · 2016
Earlier work this paper cites.
Vision-based and marker-less surgical tool detection and tracking: a review of the literature
Bouget, D., Allan, M., Stoyanov, D., Jannin, P., 2017 · 2017
Earlier work this paper cites.
Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp. 2961–2969
He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017 · 2017
Earlier work this paper cites.
Simple does it: Weakly supervised instance and semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 876–885
Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B., 2017 · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A., Valpola, H., 2017 · 2017
Earlier work this paper cites.
Learning random-walk label propagation for weakly-supervised semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7158–7166
Vernaza, P., Chandraker, M., 2017 · 2017
Earlier work this paper cites.
Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M.H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A.K., Marti, R., 2017 · 2017
Earlier work this paper cites.
Hybrid high-order functional connectivity networks using resting-state functional mri for mild cognitive impairment diagnosis
Zhang, Y., Zhang, H., Chen, X., Lee, S.W., Shen, D., 2017 · 2017
Earlier work this paper cites.
Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4981–4990
Ahn, J., Kwak, S., 2018 · 2018
Earlier work this paper cites.
Adversarial similarity network for evaluating image alignment in deep learning based registration, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, Springer. pp. 739–746
Fan, J., Cao, X., Xue, Z., Yap, P.T., Shen, D., 2018 · 2018
Earlier work this paper cites.
Structured reporting in radiology
Ganeshan, D., Duong, P.A.T., Probyn, L., Lenchik, L., McArthur, T.A., Retrouvey, M., Ghobadi, E.H., Desouches, S.L., Pastel, D., Francis, I.R., 2018 · 2018
Earlier work this paper cites.
Identifying medical diagnoses and treatable diseases by image-based deep learning
Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., et al., 2018 · 2018
Earlier work this paper cites.
An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection
Liu, F., Liu, C., Zhao, L., Zhang, X., Wu, X., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., et al., 2018 · 2018
Earlier work this paper cites.
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative cta images using deep convolutional neural networks
López-Linares, K., Aranjuelo, N., Kabongo, L., Maclair, G., Lete, N., Ceresa, M., García-Familiar, A., Macía, I., Ballester, M.A.G., 2018 · 2018
Earlier work this paper cites.
Y-net: joint segmentation and classification for diagnosis of breast biopsy images, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, Springer. pp. 893–901
Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L., 2018 · 2018
Earlier work this paper cites.
Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3d ct data, in: Proceedings of the European conference on computer vision (ECCV) Workshops, pp. 0–0
O’Neil, A.Q., Kascenas, A., Henry, J., Wyeth, D., Shepherd, M., Beveridge, E., Clunie, L., Sansom, C., Seduikyte Keith Muir, E., Poole, I., 2018 · 2018
Earlier work this paper cites.
Learn the new, keep the old: Extending pretrained models with new anatomy and images, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11, Springer. pp. 361–369
Ozdemir, F., Fuernstahl, P., Goksel, O., 2018 · 2018
Earlier work this paper cites.
Radiology objects in context (roco): a multimodal image dataset, in: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3, Springer. pp. 180–189
Pelka, O., Koitka, S., Rückert, J., Nensa, F., Friedrich, C.M., 2018 · 2018
Earlier work this paper cites.
Negbio: a high-performance tool for negation and uncertainty detection in radiology reports
Peng, Y., Wang, X., Lu, L., Bagheri, M., Summers, R., Lu, Z., 2018 · 2018
Earlier work this paper cites.
Detecting and classifying lesions in mammograms with deep learning
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I., 2018 · 2018
Earlier work this paper cites.
Graph attention networks, in: International Conference on Learning Representations
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y., 2018 · 2018
Earlier work this paper cites.
Publicly available clinical, in: Proceedings of the 2nd Clinical Natural Language Processing Workshop, Association for Computational Linguistics
Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jindi, D., Naumann, T., McDermott, M., 2019 · 2019
Earlier work this paper cites.
Scibert: A pretrained language model for scientific text, in: EMNLP, Association for Computational Linguistics
Beltagy, I., Lo, K., Cohan, A., 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Minneapolis, Minnesota. pp. 4171–4186
Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2019 · 2019
Earlier work this paper cites.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, in: Proceedings of the AAAI conference on artificial intelligence, pp. 590–597
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al., 2019 · 2019
Earlier work this paper cites.
Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., Deng, C.y., Mark, R.G., Horng, S., 2019 · 2019
Earlier work this paper cites.
Aptos 2019 blindness detection
Karthik, Maggie, S.D., 2019 · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of NAACL-HLT, pp. 4171–4186
Kenton, J.D.M.W.C., Toutanova, L.K., 2019 · 2019
Earlier work this paper cites.
Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease
LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A.G., et al., 2019 · 2019
Earlier work this paper cites.
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Lessmann, N., Van Ginneken, B., De Jong, P.A., Išgum, I., 2019 · 2019
Earlier work this paper cites.
Computational neuroanatomy of baby brains: A review
Li, G., Wang, L., Yap, P.T., Wang, F., Wu, Z., Meng, Y., Dong, P., Kim, J., Shi, F., Rekik, I., et al., 2019 · 2019
Earlier work this paper cites.
Clinically accurate chest x-ray report generation, in: Machine Learning for Healthcare Conference, PMLR. pp. 249–269
Liu, G., Hsu, T.M.H., McDermott, M., Boag, W., Weng, W.H., Szolovits, P., Ghassemi, M., 2019 · 2019
Earlier work this paper cites.
Extending pretrained segmentation networks with additional anatomical structures
Ozdemir, F., Goksel, O., 2019 · 2019
Earlier work this paper cites.
A survey on biomedical image captioning, in: Proceedings of the second workshop on shortcomings in vision and language, pp. 26–36
Pavlopoulos, J., Kougia, V., Androutsopoulos, I., 2019 · 2019
Earlier work this paper cites.
Human uncertainty makes classification more robust, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9617–9626
Peterson, J.C., Battleday, R.M., Griffiths, T.L., Russakovsky, O., 2019 · 2019
Earlier work this paper cites.
Capturing human categorization of natural images by combining deep networks and cognitive models
Battleday, R.M., Peterson, J.C., Griffiths, T.L., 2020 · 2020
Earlier work this paper cites.
Padchest: A large chest x-ray image dataset with multi-label annotated reports
Bustos, A., Pertusa, A., Salinas, J.M., De La Iglesia-Vaya, M., 2020 · 2020
Earlier work this paper cites.
Generating radiology reports via memory-driven transformer, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1439–1449
Chen, Z., Song, Y., Chang, T.H., Wan, X., 2020b · 2020
Earlier work this paper cites.
Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Fang, X., Yan, P., 2020 · 2020
Earlier work this paper cites.
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F.A., 2020 · 2020
Earlier work this paper cites.
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Lauritsen, S.M., Kristensen, M., Olsen, M.V., Larsen, M.S., Lauritsen, K.M., Jørgensen, M.J., Lange, J., Thiesson, B., 2020 · 2020
Earlier work this paper cites.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J., 2020 · 2020
Earlier work this paper cites.
Multi-resolution convolutional networks for chest x-ray radiograph based lung nodule detection
Li, X., Shen, L., Xie, X., Huang, S., Xie, Z., Hong, X., Yu, J., 2020 · 2020
Earlier work this paper cites.
Ms-net: multi-site network for improving prostate segmentation with heterogeneous mri data
Liu, Q., Dou, Q., Yu, L., Heng, P.A., 2020 · 2020
Earlier work this paper cites.
Identifying main finding sentences in clinical case reports
Luo, M., Cohen, A.M., Addepalli, S., Smalheiser, N.R., 2020 · 2020
Earlier work this paper cites.
Evolving multi-label classification rules by exploiting high-order label correlations
Nazmi, S., Yan, X., Homaifar, A., Doucette, E., 2020 · 2020
Earlier work this paper cites.
Learning hierarchical attention for weakly-supervised chest x-ray abnormality localization and diagnosis
Ouyang, X., Karanam, S., Wu, Z., Chen, T., Huo, J., Zhou, X.S., Wang, Q., Cheng, J.Z., 2020 · 2020
Earlier work this paper cites.
Coarse to fine vertebrae localization and segmentation with spatialconfiguration-net and u-net., in: VISIGRAPP (5: VISAPP), pp. 124–133
Payer, C., Stern, D., Bischof, H., Urschler, M., 2020 · 2020
Earlier work this paper cites.
Conditional convolutions for instance segmentation, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, Springer. pp. 282–298
Tian, Z., Shen, C., Chen, H., 2020 · 2020
Cited alongside, same era.
A survey on explainable artificial intelligence (xai): Toward medical xai
Tjoa, E., Guan, C., 2020 · 2020
Cited alongside, same era.
Ptb-xl, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.D., Kreiseler, D., Lunze, F.I., Samek, W., Schaeffter, T., 2020 · 2020
Cited alongside, same era.
Focalmix: Semi-supervised learning for 3d medical image detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3951–3960
Wang, D., Zhang, Y., Zhang, K., Wang, L., 2020 · 2020
Cited alongside, same era.
Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in ct
Yan, K., Cai, J., Zheng, Y., Harrison, A.P., Jin, D., Tang, Y., Tang, Y., Huang, L., Xiao, J., Lu, L., 2020 · 2020
Pathology-and-genomics multimodal transformer for survival outcome prediction, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 622–631
Ding, K., Zhou, M., Metaxas, D.N., Zhang, S., 2023 · 2023
Closest in time.
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
Closest in time.
Synthetic data accelerates the development of generalizable learning-based algorithms for x-ray image analysis
Gao, C., Killeen, B.D., Hu, Y., Grupp, R.B., Taylor, R.H., Armand, M., Unberath, M., 2023 · 2023
Closest in time.
A systematic survey of prompt engineering on vision-language foundation models
Gu, J., Han, Z., Chen, S., Beirami, A., He, B., Zhang, G., Liao, R., Qin, Y., Tresp, V., Torr, P., 2023 · 2023
Closest in time.
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Cited alongside, same era.
When radiology report generation meets knowledge graph, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12910–12917
Zhang, Y., Wang, X., Xu, Z., Yu, Q., Yuille, A., Xu, D., 2020 · 2020
Cited alongside, same era.
Big self-supervised models advance medical image classification
Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al., 2021 · 2021
Cited alongside, same era.
nndetection: a self-configuring method for medical object detection, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24, Springer. pp. 530–539
Baumgartner, M., Jäger, P.F., Isensee, F., Maier-Hein, K.H., 2021 · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021 · 2021
Cited alongside, same era.
Exploring simple siamese representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758
Chen, X., He, K., 2021 · 2021
Cited alongside, same era.
Learning continuous image representation with local implicit image function, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8628–8638
Chen, Y., Liu, S., Wang, X., 2021 · 2021
Cited alongside, same era.
Eslami, S., de Melo, G., Meinel, C., 2021 · 2021
Cited alongside, same era.
Multiple prompt fusion for zero-shot lesion detection using vision-language models, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 283–292
Guo, M., Yi, H., Qin, Z., Wang, H., Men, A., Lao, Q., 2023 · 2023
Closest in time.
Generatect: Text-conditional generation of 3d chest ct volumes
Hamamci, I.E., Er, S., Sekuboyina, A., Simsar, E., Tezcan, A., Simsek, A.G., Esirgun, S.N., Almas, F., Dogan, I., Dasdelen, M.F., et al., 2023 · 2023
Closest in time.
Label-free liver tumor segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7422–7432
Hu, Q., Chen, Y., Xiao, J., Sun, S., Chen, J., Yuille, A.L., Zhou, Z., 2023 · 2023
Closest in time.
Taming detection transformers for medical object detection, in: BVM Workshop, Springer. pp. 183–188
Ickler, M.K., Baumgartner, M., Roy, S., Wald, T., Maier-Hein, K.H., 2023 · 2023
Closest in time.
Quilt-1m: One million image-text pairs for histopathology
Ikezogwo, W.O., Seyfioglu, M.S., Ghezloo, F., Geva, D.S.C., Mohammed, F.S., Anand, P.K., Krishna, R., Shapiro, L., 2023 · 2023
Closest in time.
Overview of the imageclef 2023: Multimedia retrieval in medical, social media and internet applications, in: International Conference of the Cross-Language Evaluation Forum for European Languages, Springer. pp. 370–396
Ionescu, B., Müller, H., Drăgulinescu, A.M., Yim, W.W., Ben Abacha, A., Snider, N., Adams, G., Yetisgen, M., Rückert, J., García Seco de Herrera, A., et al., 2023 · 2023
Closest in time.
Diffusion models in medical imaging: A comprehensive survey
Kazerouni, A., Aghdam, E.K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., Merhof, D., 2023 · 2023
Closest in time.
Maple: Multi-modal prompt learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19113–19122
Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S., 2023 · 2023
Closest in time.
Concept bottleneck with visual concept filtering for explainable medical image classification
Kim, I., Kim, J., Choi, J., Kim, H.J., 2023 · 2023
Closest in time.
Clip-lung: Textual knowledge-guided lung nodule malignancy prediction, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Springer Nature Switzerland, Cham. pp. 403–412
Lei, Y., Li, Z., Shen, Y., Zhang, J., Shan, H., 2023c · 2023
Closest in time.
Gridclip: One-stage object detection by grid-level clip representation learning
Lin, J., Gong, S., 2023 · 2023
Closest in time.
Learning hierarchical-order functional connectivity networks for mild cognitive impairment diagnosis, in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 1–5
Liu, Y., Liu, M., Zhang, Y., Shen, D., 2023h · 2023
Closest in time.
Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation, in: International Conference on Machine Learning, PMLR. pp. 23033–23044
Luo, H., Bao, J., Wu, Y., He, X., Li, T., 2023 · 2023
Closest in time.
Local feature matters: Cascade multi-scale mlp for edge segmentation of medical images
Lv, J., Hu, Y., Fu, Q., Hu, Y., Lv, L., Yang, G., Li, J., Zhao, Y., 2023 · 2023
Closest in time.
Understanding zero-shot adversarial robustness for large-scale models, in: The Eleventh International Conference on Learning Representations
Mao, C., Geng, S., Yang, J., Wang, X., Vondrick, C., 2023 · 2023
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Mishra, A., Mittal, R., Jestin, C., Tingos, K., Rajpurkar, P., 2023 · 2023
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Med-flamingo: a multimodal medical few-shot learner, in: Machine Learning for Health (ML4H), PMLR. pp. 353–367
Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Reis, E.P., Rajpurkar, P., 2023 · 2023
Closest in time.
Anatomy-driven pathology detection on chest x-rays, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 57–66
Müller, P., Meissen, F., Brandt, J., Kaissis, G., Rueckert, D., 2023 · 2023
Closest in time.
Tier: Text-image entropy regularization for medical clip-style models, in: Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR. pp. 548–564
Palepu, A., Beam, A., 2023 · 2023
Closest in time.
Enhancing automatic placenta analysis through distributional feature recomposition in vision-language contrastive learning, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 116–126
Pan, Y., Cai, T., Mehta, M., Gernand, A.D., Goldstein, J.A., Mithal, L., Mwinyelle, D., Gallagher, K., Wang, J.Z., 2023 · 2023
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Xplainer: From x-ray observations to explainable zero-shot diagnosis
Pellegrini, C., Keicher, M., Özsoy, E., Jiraskova, P., Braren, R., Navab, N., 2023 · 2023
Closest in time.
Abdomenatlas-8k: Annotating 8,000 ct volumes for multi-organ segmentation in three weeks, in: Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Qu, C., Zhang, T., Qiao, H., Liu, J., Tang, Y., Yuille, A., Zhou, Z., 2023 · 2023
Closest in time.
Smallcap: lightweight image captioning prompted with retrieval augmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2840–2849
Ramos, R., Martins, B., Elliott, D., Kementchedjhieva, Y., 2023 · 2023
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Image synthesis with disentangled attributes for chest x-ray nodule augmentation and detection
Shen, Z., Ouyang, X., Xiao, B., Cheng, J.Z., Shen, D., Wang, Q., 2023 · 2023
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Medical vision language pretraining: A survey
Shrestha, P., Amgain, S., Khanal, B., Linte, C.A., Bhattarai, B., 2023 · 2023
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Multi-label classification with high-rank and high-order label correlations
Si, C., Jia, Y., Wang, R., Zhang, M.L., Feng, Y., Chongxiao, Q., 2023 · 2023
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Open-ended medical visual question answering through prefix tuning of language models
van Sonsbeek, T., Derakhshani, M.M., Najdenkoska, I., Snoek, C.G., Worring, M., 2023 · 2023
Closest in time.
X-tra: Improving chest x-ray tasks with cross-modal retrieval augmentation, in: International Conference on Information Processing in Medical Imaging, Springer. pp. 471–482
van Sonsbeek, T., Worring, M., 2023 · 2023
Closest in time.
Eva-clip: Improved training techniques for clip at scale
Sun, Q., Fang, Y., Wu, L., Wang, X., Cao, Y., 2023 · 2023
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Interactive and explainable region-guided radiology report generation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7433–7442
Tanida, T., Müller, P., Kaissis, G., Rueckert, D., 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al., 2023 · 2023
Closest in time.
Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias, in: Thirty-seventh Conference on Neural Information Processing Systems
Wan, Z., Liu, C., Zhang, M., Fu, J., Wang, B., Cheng, S., Ma, L., Quilodrán-Casas, C., Arcucci, R., 2023 · 2023
Closest in time.
Umcl: Unified medical image-text-label contrastive learning with continuous prompt, in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 2285–2289
Wang, Y., Wang, G., 2023 · 2023
Closest in time.
Totalsegmentator: robust segmentation of 104 anatomic structures in ct images
Wasserthal, J., Breit, H.C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., et al., 2023 · 2023
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Medklip: Medical knowledge enhanced language-image pre-training
Wu, C., Zhang, X., Zhang, Y., Wang, Y., Xie, W., 2023a · 2023
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Medim: Boost medical image representation via radiology report-guided masking, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 13–23
Xie, Y., Gu, L., Harada, T., Zhang, J., Xia, Y., Wu, Q., 2023 · 2023
Closest in time.
Doctorglm: Fine-tuning your chinese doctor is not a herculean task
Xiong, H., Wang, S., Zhu, Y., Zhao, Z., Liu, Y., Wang, Q., Shen, D., 2023 · 2023
Closest in time.
Robust and interpretable medical image classifiers via concept bottleneck models
Yan, A., Wang, Y., Zhong, Y., He, Z., Karypis, P., Wang, Z., Dong, C., Gentili, A., Hsu, C.N., Shang, J., et al., 2023 · 2023
Closest in time.
Tceip: Text condition embedded regression network for dental implant position prediction, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 317–326
Yang, X., Xie, J., Li, X., Li, X., Li, X., Shen, L., Deng, Y., 2023 · 2023
Closest in time.
Cxr-clip: Toward large scale chest x-ray language-image pre-training, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 101–111
You, K., Gu, J., Ham, J., Park, B., Kim, J., Hong, E.K., Baek, W., Roh, B., 2023 · 2023
Closest in time.
Evaluating progress in automatic chest x-ray radiology report generation
Yu, F., Endo, M., Krishnan, R., Pan, I., Tsai, A., Reis, E.P., Fonseca, E.K.U.N., Lee, H.M.H., Abad, Z.S.H., Ng, A.Y., et al., 2023 · 2023
Closest in time.
Learning multi-modal representations by watching hundreds of surgical video lectures
Yuan, K., Srivastav, V., Yu, T., Lavanchy, J., Mascagni, P., Navab, N., Padoy, N., 2023 · 2023
Closest in time.
On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., Metaxas, D., 2023 · 2023
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Cross-modal translation and alignment for survival analysis, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21485–21494
Zhou, F., Chen, H., 2023 · 2023
Closest in time.
Advancing radiograph representation learning with masked record modeling, in: The Eleventh International Conference on Learning Representations
Zhou, H.Y., Lian, C., Wang, L., Yu, Y., 2023 · 2023
Closest in time.
Multimodal c4: An open, billion-scale corpus of images interleaved with text
Zhu, W., Hessel, J., Awadalla, A., Gadre, S.Y., Dodge, J., Fang, A., Yu, Y., Schmidt, L., Wang, W.Y., Choi, Y., 2023 · 2023
Closest in time.
Merlin: A vision language foundation model for 3d computed tomography
Blankemeier, L., Cohen, J.P., Kumar, A., Van Veen, D., Gardezi, S.J.S., Paschali, M., Chen, Z., Delbrouck, J.B., Reis, E., Truyts, C., et al., 2024 · 2024
Closest in time.
Domain-controlled prompt learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 936–944
Cao, Q., Xu, Z., Chen, Y., Ma, C., Yang, X., 2024 · 2024
Closest in time.
Chexpert plus: Hundreds of thousands of aligned radiology texts, images and patients
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Vision–language foundation model for echocardiogram interpretation
Christensen, M., Vukadinovic, M., Yuan, N., Ouyang, D., 2024 · 2024
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Hamamci, I.E., Er, S., Almas, F., Simsek, A.G., Esirgun, S.N., Dogan, I., Dasdelen, M.F., Wittmann, B., Simsar, E., Simsar, M., et al., 2024 · 2024
Closest in time.
Towards long-tailed, multi-label disease classification from chest x-ray: Overview of the cxr-lt challenge
Holste, G., Zhou, Y., Wang, S., Jaiswal, A., Lin, M., Zhuge, S., Yang, Y., Kim, D., Nguyen-Mau, T.H., Tran, M.T., et al., 2024 · 2024
Closest in time.
Ketabi, S., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F., 2024 · 2024
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Transparent medical image ai via an image–text foundation model grounded in medical literature
Kim, C., Gadgil, S.U., DeGrave, A.J., Omiye, J.A., Cai, Z.R., Daneshjou, R., Lee, S.I., 2024 · 2024
Closest in time.
Gaze-detr: Using expert gaze to reduce false positives in vulvovaginal candidiasis screening
Kong, Y., Wang, S., Cai, J., Zhao, Z., Shen, Z., Li, Y., Fei, M., Wang, Q., 2024 · 2024
Closest in time.
Improving medical multi-modal contrastive learning with expert annotations, in: Computer Vision–ECCV 2024: 18th European Conference, Zurich, Switzerland, Springer
Kumar, Y., Marttinen, P., 2024 · 2024
Closest in time.
Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., Gao, J., 2024 · 2024
Closest in time.
Lin, J., Xia, Y., Zhang, J., Yan, K., Lu, L., Luo, J., Zhang, L., 2024 · 2024
Closest in time.
Etp: Learning transferable ecg representations via ecg-text pre-training, in: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 8230–8234
Liu, C., Wan, Z., Cheng, S., Zhang, M., Arcucci, R., 2024a · 2024
Closest in time.
Fairclip: Harnessing fairness in vision-language learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12289–12301
Luo, Y., Shi, M., Khan, M.O., Afzal, M.M., Huang, H., Yuan, S., Tian, Y., Song, L., Kouhana, A., Elze, T., et al., 2024 · 2024
Closest in time.
Synthesizing a β \beta -pet via an image and label conditioning latent diffusion model for detecting amyloid status, in: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 6610–6614
Ou, Z., Pan, Y., Li, Y., Xie, F., Guo, Q., Shen, D., 2024 · 2024
Closest in time.
I-ai: A controllable & interpretable ai system for decoding radiologists’ intense focus for accurate cxr diagnoses, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 7850–7859
Pham, T.T., Brecheisen, J., Nguyen, A., Nguyen, H., Le, N., 2024 · 2024
Closest in time.
Exploring transfer learning in medical image segmentation using vision-language models, in: Medical Imaging with Deep Learning
Poudel, K., Dhakal, M., Bhandari, P., Adhikari, R., Thapaliya, S., Khanal, B., 2024 · 2024
Closest in time.
Robust clip: Unsupervised adversarial fine-tuning of vision embeddings for robust large vision-language models
Schlarmann, C., Singh, N.D., Croce, F., Hein, M., 2024 · 2024
Closest in time.
Knowledge-guided prompt learning for lifespan brain mr image segmentation
Teng, L., Zhao, Z., Huang, J., Cao, Z., Meng, R., Shi, F., Shen, D., 2024 · 2024
Closest in time.
Demonstration of an adversarial attack against a multimodal vision language model for pathology imaging, in: 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), IEEE
Thota, P., Veerla, J., Guttikonda, P., Nasr, M.S., Nilizadeh, S., Luber, J.M., 2024 · 2024
Closest in time.
On large visual language models for medical imaging analysis: An empirical study
Van, M.H., Verma, P., Wu, X., 2024 · 2024
Closest in time.
Vukadinovic, M., Tang, X., Yuan, N., Cheng, P., Li, D., Cheng, S., He, B., Ouyang, D., 2024 · 2024
Closest in time.
Mm-retinal: Knowledge-enhanced foundational pretraining with fundus image-text expertise, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 722–732
Wu, R., Zhang, C., Zhang, J., Zhou, Y., Zhou, T., Fu, H., 2024 · 2024
Closest in time.
Medsyn: Text-guided anatomy-aware synthesis of high-fidelity 3d ct images
Xu, Y., Sun, L., Peng, W., Jia, S., Morrison, K., Perer, A., Zandifar, A., Visweswaran, S., Eslami, M., Batmanghelich, K., 2024 · 2024
Closest in time.
Exploring low-resource medical image classification with weakly supervised prompt learning
Zheng, F., Cao, J., Yu, W., Chen, Z., Xiao, N., Lu, Y., 2024 · 2024
Closest in time.
AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection, in: The Twelfth International Conference on Learning Representations
Zhou, Q., Pang, G., Tian, Y., He, S., Chen, J., 2024 · 2024
Closest in time.
Radgpt: Constructing 3d image-text tumor datasets
Bassi, P.R., Yavuz, M.C., Wang, K., Chen, X., Li, W., Decherchi, S., Cavalli, A., Yang, Y., Yuille, A., Zhou, Z., 2025 · 2025
Closest in time.
Contextmri: Enhancing compressed sensing mri through metadata conditioning
Chung, H., Lee, D., Wu, Z., Kim, B.H., Bouman, K.L., Ye, J.C., 2025 · 2025
Closest in time.
Bridged semantic alignment for zero-shot 3d medical image diagnosis
Lai, H., Jiang, Z., Yao, Q., Wang, R., He, Z., Tao, X., Wei, W., Lv, W., Zhou, S.K., 2025 · 2025
Closest in time.
Fetalclip: A visual-language foundation model for fetal ultrasound image analysis
Maani, F., Saeed, N., Saleem, T., Farooq, Z., Alasmawi, H., Diehl, W., Mohammad, A., Waring, G., Valappi, S., Bricker, L., Yaqub, M., 2025 · 2025
Closest in time.
Large-scale and fine-grained vision-language pre-training for enhanced CT image understanding, in: The Thirteenth International Conference on Learning Representations
Shui, Z., Zhang, J., Cao, W., Wang, S., Guo, R., Lu, L., Zhang, L., Liang, T., Yang, L., Ye, X., Zhang, Q., 2025 · 2025
Closest in time.
A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision
Silva-Rodriguez, J., Chakor, H., Kobbi, R., Dolz, J., Ayed, I.B., 2025 · 2025
Closest in time.
Pathgen-1.6m: 1.6 million pathology image-text pairs generation through multi-agent collaboration, in: The Thirteenth International Conference on Learning Representations
Sun, Y., Zhang, Y., Si, Y., Zhu, C., Zhang, K., Shui, Z., Li, J., Gong, X., LYU, X., Lin, T., Yang, L., 2025 · 2025
Closest in time.
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
Turgut, Ö., Müller, P., Hager, P., Shit, S., Starck, S., Menten, M.J., Martens, E., Rueckert, D., 2025 · 2025
Closest in time.
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., et al., 2025 · 2025
Closest in time.
High-order correlation preserved incomplete multi-view subspace clustering
Li, Z., Tang, C., Zheng, X., Liu, X., Zhang, W., Zhu, E., 2022d · 2080
Closest in time.
Collaborative learning of semi-supervised segmentation and classification for medical images, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2079–2088
Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., Shao, L., 2019 · 2088
Closest in time.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097–2106
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., 2017 · 2097
Closest in time.