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Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task.
Schmidhuber, J.: Evolutionary Principles in Self-referential Learning. (1987)
1987
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP (2014)
2014
Earlier work this paper cites.
Bar, Y., Diamant, I., Wolf, L., Greenspan, H.: Deep learning with non-medical training used for chest pathology identification. In: Medical Imaging: Computer-Aided Diagnosis (2015)
2015
Earlier work this paper cites.
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
2015
Earlier work this paper cites.
Fukui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M.: Multimodal compact bilinear pooling for visual question answering and visual grounding. In: EMNLP (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: ICML (2016)
2016
Earlier work this paper cites.
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS (2016)
2016
Earlier work this paper cites.
Wang, Y.X., Hebert, M.: Learning from small sample sets by combining unsupervised meta-training with cnns. In: NIPS (2016)
2016
Earlier work this paper cites.
Yang, Z., He, X., Gao, J., Deng, L., Smola, A.J.: Stacked attention networks for image question answering. In: CVPR (2016)
2016
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)
2017
Earlier work this paper cites.
Munkhdalai, T., Yu, H.: Meta networks. In: ICML (2017)
2017
Earlier work this paper cites.
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NIPS (2017)
2017
Cited alongside, same era.
Abacha, A.B., Gayen, S., Lau, J.J., Rajaraman, S., Demner-Fushman, D.: NLM at ImageCLEF 2018 visual question answering in the medical domain. CEUR Workshop Proceedings (2018)
2018
Cited alongside, same era.
Kim, J.H., Jun, J., Zhang, B.T.: Bilinear attention networks. In: NIPS (2018)
2018
Cited alongside, same era.
Lau, J.J., Gayen, S., Abacha, A.B., Demner-Fushman, D.: A dataset of clinically generated visual questions and answers about radiology images. Nature (2018)
2018
Cited alongside, same era.
Maicas, G., Bradley, A.P., Nascimento, J.C., Reid, I., Carneiro, G.: Training medical image analysis systems like radiologists. In: MICCAI (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Nguyen, B.D., Do, T.T., Nguyen, B.X., Do, T., Tjiputra, E., Tran, Q.D.: Overcoming data limitation in medical visual question answering. In: MICCAI (2019)
2019
Later among the works it cites.
Shi, L., Liu, F., Rosen, M.P.: Deep multimodal learning for medical visual question answering. In: CLEF (Working Notes) (2019)
2019
Later among the works it cites.
Vu, M., Sznitman, R., Nyholm, T., Löfstedt, T.: Ensemble of streamlined bilinear visual question answering models for the imageclef 2019 challenge in the medical domain. In: Conference and Labs of the Evaluation Forum (2019)
2019
Later among the works it cites.
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2018
Cited alongside, same era.
Peng, Y., Liu, F., Rosen, M.P.: Umass at imageclef medical visual question answering (med-vqa) 2018 task. CEUR Workshop Proceedings (2018)
2018
Cited alongside, same era.
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: CVPR (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Abacha, A.B., Hasan, S.A., Datla, V.V., Liu, J., Demner-Fushman, D., Müller, H.: Vqa-med: Overview of the medical visual question answering task at imageclef 2019. In: CLEF (Working Notes) (2019)
2019
Cited alongside, same era.
Do, T., Do, T.T., Tran, H., Tjiputra, E., Tran, Q.D.: Compact trilinear interaction for visual question answering. In: ICCV (2019)
2019
Cited alongside, same era.
Hsu, K., Levine, S., Finn, C.: Unsupervised learning via meta-learning. In: ICLR (2019)
2019
Cited alongside, same era.
Chi, W., Dagnino, G., Kwok, T.M., Nguyen, A., Kundrat, D., Abdelaziz, E., Riga, C., Bicknell, C., Yang, G.Z.: Collaborative robot-assisted endovascular catheterization with generative adversarial imitation learning. In: ICRA (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Huang, B., Tsai, Y.Y., Cartucho, J., Vyas, K., Tuch, D., Giannarou, S., Elson, D.S.: Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe. International Journal of Computer Assisted Radiology and Surgery (2020)
2020
Later among the works it cites.
Liu, S., Ding, H., Zhou, X.: Shengyan at vqa-med 2020: An encoder-decoder model for medical domain visual question answering task. CLEF (2020)
2020
Later among the works it cites.
Nguyen, A., Kundrat, D., Dagnino, G., Chi, W., Abdelaziz, E., Guo, Y., Ma, Y., Kwok, T., Riga, C., Yang, G.Z.: End-to-end real-time catheter segmentation with optical flow-guided warping during endovascular intervention. In: ICRA (2020)
2020
Later among the works it cites.
Nguyen, A., Nguyen, N., Tran, K., Tjiputra, E., Tran, Q.: Autonomous navigation in complex environments with deep multimodal fusion network. In: IROS (2020)
2020
Later among the works it cites.
Ren, F., Zhou, Y.: Cgmvqa: a new classification and generative model for medical visual question answering. IEEE Access (2020)
2020
Later among the works it cites.
Vu, M.H., Löfstedt, T., Nyholm, T., Sznitman, R.: A question-centric model for visual question answering in medical imaging. IEEE TMI (2020)
2020
Later among the works it cites.
Zhan, L.M., Liu, B., Fan, L., Chen, J., Wu, X.M.: Medical visual question answering via conditional reasoning. In: ACM International Conference on Multimedia (2020)
2020
Later among the works it cites.
Gupta, D., Suman, S., Ekbal, A.: Hierarchical deep multi-modal network for medical visual question answering. Expert Systems with Applications (2021)
2021
Closest in time.
Huang, B., Zheng, J.Q., Nguyen, A., Tuch, D., Vyas, K., Giannarou, S., Elson, D.: Self-supervised generative adversarial network for depth estimation in laparoscopic images. In: MICCAI (2021)
2021
Closest in time.