Fetching the paper…
Reading the bibliography…
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods.
Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R.D., Buchwalter, W., 2019 · 1906
Earlier work this paper cites.
Self-labelling via simultaneous clustering and representation learning
Asano, Y.M., Rupprecht, C., Vedaldi, A., 2019 · 1911
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020 · 1911
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., He, K., 2020c · 2003
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A., 2020 · 2006
Earlier work this paper cites.
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.Á., Guo, Z.D., Azar, M.G., Piot, B., Kavukcuoglu, K., Munos, R., Valko, M., 2020b · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), IEEE. pp. 1735–1742
Hadsell, R., Chopra, S., LeCun, Y., 2006 · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Modeling and segmentation of surgical workflow from laparoscopic video, in: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III, Springer. pp. 400–407
Blum, T., Feußner, H., Navab, N., 2010 · 2010
Earlier work this paper cites.
Statistical modeling and recognition of surgical workflow
Padoy, N., Blum, T., Ahmadi, S., Feußner, H., Berger, M., Navab, N., 2012 · 2010
Earlier work this paper cites.
HMDB: A large video database for human motion recognition, in: Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.V. (Eds.), IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011, IEEE Computer Society. pp. 2556–2563
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T.A., Serre, T., 2011 · 2011
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M., 2012 · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport, in: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pp. 2292–2300
Cuturi, M., 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction, in: Proceedings of the IEEE international conference on computer vision, pp. 1422–1430
Doersch, C., Gupta, A., Efros, A.A., 2015 · 2015
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture, in: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2650–2658
Eigen, D., Fergus, R., 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J., et al., 2015 · 2015
Earlier work this paper cites.
Automatic data-driven real-time segmentation and recognition of surgical workflow
Dergachyova, O., Bouget, D., Huaulmé, A., Morandi, X., Jannin, P., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: CVPR
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Shuffle and learn: Unsupervised learning using temporal order verification, in: Leibe, B., Matas, J., Sebe, N., Welling, M. (Eds.), Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I, Springer. pp. 527–544
Misra, I., Zitnick, C.L., Hebert, M., 2016 · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles, in: European conference on computer vision, Springer. pp. 69–84
Noroozi, M., Favaro, P., 2016 · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society. pp. 2536–2544
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A., 2016 · 2016
Earlier work this paper cites.
Single- and multi-task architecture for surgical workflow at m2cai 2016
Twinanda, A.P., Mutter, D., Marescaux, J., Mathelin, M., Padoy, N., 2016a · 2016
Earlier work this paper cites.
Colorful image colorization, in: European conference on computer vision, Springer. pp. 649–666
Zhang, R., Isola, P., Efros, A.A., 2016 · 2016
Earlier work this paper cites.
Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis
Bodenstedt, S., Wagner, M., Katic, D., Mietkowski, P., Mayer, B., Kenngott, H., Müller-Stich, B., Dillmann, R., Speidel, S., 2017 · 2017
Earlier work this paper cites.
Quo vadis, action recognition? A new model and the kinetics dataset, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, IEEE Computer Society. pp. 4724–4733
Carreira, J., Zisserman, A., 2017 · 2017
Earlier work this paper cites.
Self-supervised video representation learning with odd-one-out networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, IEEE Computer Society. pp. 5729–5738
Fernando, B., Bilen, H., Gavves, E., Gould, S., 2017 · 2017
Earlier work this paper cites.
Sv-rcnet: Workflow recognition from surgical videos using recurrent convolutional network
Jin, Y., Dou, Q., Chen, H., Yu, L., Qin, J., Fu, C., Heng, P., 2018 · 2017
Earlier work this paper cites.
Unsupervised representation learning by sorting sequences, in: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, IEEE Computer Society. pp. 667–676
Lee, H., Huang, J., Singh, M., Yang, M., 2017 · 2017
Earlier work this paper cites.
Surgical data science for next-generation interventions
Maier-Hein, L., Vedula, S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feussner, H., Forestier, G., Giannarou, S., Hashizume, M., Katić, D., Kenngott, H., Kranzfelder, M., Malpani, A., März, K., Neumuth, T., Padoy, N., Pugh, C., Jannin, P., 2017 · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data, in: Artificial intelligence and statistics, PMLR. pp. 1273–1282
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A., 2017 · 2017
Earlier work this paper cites.
Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al., 2017 · 2017
Earlier work this paper cites.
Neural discrete representation learning, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA. p. 6309–6318
van den Oord, A., Vinyals, O., Kavukcuoglu, K., 2017 · 2017
Earlier work this paper cites.
Learning features by watching objects move, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, IEEE Computer Society. pp. 6024–6033
Pathak, D., Girshick, R.B., Dollár, P., Darrell, T., Hariharan, B., 2017 · 2017
Earlier work this paper cites.
Large batch training of convolutional networks
You, Y., Gitman, I., Ginsburg, B., 2017 · 2017
Earlier work this paper cites.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1058–1067
Zhang, R., Isola, P., Efros, A.A., 2017 · 2017
Earlier work this paper cites.
Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks
Al Hajj, H., Lamard, M., Conze, P.H., Cochener, B., Quellec, G., 2018 · 2018
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149
Caron, M., Bojanowski, P., Joulin, A., Douze, M., 2018 · 2018
Earlier work this paper cites.
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis, in: OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis. Springer, pp. 85–93
Funke, I., Jenke, A., Mees, S.T., Weitz, J., Speidel, S., Bodenstedt, S., 2018 · 2018
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., Komodakis, N., 2018 · 2018
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y., 2018 · 2018
Cited alongside, same era.
Learning image representations by completing damaged jigsaw puzzles, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. pp. 793–802
Kim, D., Cho, D., Yoo, D., Kweon, I.S., 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Van den Oord, A., Li, Y., Vinyals, O., 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A.v.d., Li, Y., Vinyals, O., 2018 · 2018
Cited alongside, same era.
Megdet: A large mini-batch object detector, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6181–6189
Perceptual codebook for bert pre-training of vision transformers
Dong, X., Bao, J., Zhang, T., Chen, D., Zhang, W., Yuan, L., Chen, D., Wen, F., Peco, N.Y., 2021 · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale, in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021 · 2021
Later among the works it cites.
Contrastive learning with continuous proxy meta-data for 3d MRI classification, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 58–68
Dufumier, B., Gori, P., Victor, J., Grigis, A., Wessa, M., Brambilla, P., Favre, P., Polosan, M., McDonald, C., Piguet, C.M., Phillips, M.L., Eyler, L., Duchesnay, E., 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., Sun, J., 2018 · 2018
Cited alongside, same era.
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Ross, T., Zimmerer, D., Vemuri, A., Isensee, F., Wiesenfarth, M., Bodenstedt, S., Both, F., Kessler, P., Wagner, M., Müller, B., et al., 2018 · 2018
Cited alongside, same era.
Tracking emerges by colorizing videos, in: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (Eds.), Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, Springer. pp. 402–419
Vondrick, C., Shrivastava, A., Fathi, A., Guadarrama, S., Murphy, K., 2018 · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3733–3742
Wu, Z., Xiong, Y., Yu, S.X., Lin, D., 2018 · 2018
Cited alongside, same era.
Yengera, G., Mutter, D., Marescaux, J., Padoy, N., 2018 · 2018
Cited alongside, same era.
DeepPhase: Surgical phase recognition in cataracts videos, in: MICCAI
Zisimopoulos, O., Flouty, E., Luengo, I., Giataganas, P., Nehme, J., Chow, A., Stoyanov, D., 2018 · 2018
Cited alongside, same era.
Video jigsaw: Unsupervised learning of spatiotemporal context for video action recognition, in: IEEE Winter Conference on Applications of Computer Vision, WACV 2019, IEEE. pp. 179–189
Ahsan, U., Madhok, R., Essa, I.A., 2019 · 2019
Cited alongside, same era.
Cataracts: Challenge on automatic tool annotation for cataract surgery
Al Hajj, H., Lamard, M., Conze, P.H., Roychowdhury, S., Hu, X., Maršalkaitė, G., Zisimopoulos, O., Dedmari, M.A., Zhao, F., Prellberg, J., et al., 2019 · 2019
Cited alongside, same era.
A large-scale study on unsupervised spatiotemporal representation learning, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, Computer Vision Foundation / IEEE. pp. 3299–3309
Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R.B., He, K., 2021b · 2021
Later among the works it cites.
Machine learning for surgical phase recognition: A systematic review
Garrow, C.R., Kowalewski, K.F., Li, L., Wagner, M., Schmidt, M.W., Engelhardt, S., Hashimoto, D.A., Kenngott, H.G., Bodenstedt, S., Speidel, S., Müller-Stich, B.P., Nickel, F., 2021 · 2021
Later among the works it cites.
Self-supervised pretraining of visual features in the wild
Goyal, P., Caron, M., Lefaudeux, B., Xu, M., Wang, P., Pai, V., Singh, M., Liptchinsky, V., Misra, I., Joulin, A., et al., 2021 · 2021
Later among the works it cites.
Cadis: Cataract dataset for surgical rgb-image segmentation
Grammatikopoulou, M., Flouty, E., Kadkhodamohammadi, A., Quellec, G., Chow, A., Nehme, J., Luengo, I., Stoyanov, D., 2021 · 2021
Later among the works it cites.
Semi-supervised contrastive learning for label-efficient medical image segmentation, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 481–490
Hu, X., Zeng, D., Xu, X., Shi, Y., 2021 · 2021
Later among the works it cites.
Lesion-based contrastive learning for diabetic retinopathy grading from fundus images, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 113–123
Huang, Y., Lin, L., Cheng, P., Lyu, J., Tang, X., 2021 · 2021
Later among the works it cites.
Temporal memory relation network for workflow recognition from surgical video
Jin, Y., Long, Y., Chen, C., Zhao, Z., Dou, Q., Heng, P.A., 2021 · 2021
Later among the works it cites.
Contrastive learning based stain normalization across multiple tumor in histopathology, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part VIII, Springer. pp. 571–580
Ke, J., Shen, Y., Liang, X., Shen, D., 2021 · 2021
Later among the works it cites.
Contrastive learning of relative position regression for one-shot object localization in 3d medical images, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 155–165
Lei, W., Xu, W., Gu, R., Fu, H., Zhang, S., Zhang, S., Wang, G., 2021 · 2021
Later among the works it cites.
Domain generalization for mammography detection via multi-style and multi-view contrastive learning, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part VII, Springer. pp. 98–108
Li, Z., Cui, Z., Wang, S., Qi, Y., Ouyang, X., Chen, Q., Yang, Y., Xue, Z., Shen, D., Cheng, J., 2021 · 2021
Later among the works it cites.
Contrastive pre-training and representation distillation for medical visual question answering based on radiology images, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 210–220
Liu, B., Zhan, L., Wu, X., 2021 · 2021
Later among the works it cites.
Surgical data science - from concepts toward clinical translation
Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feussner, H., Giannarou, S., Mascagni, P., Nakawala, H., Park, A., Pugh, C.M., Stoyanov, D., Vedula, S.S., Cleary, K., Fichtinger, G., Forestier, G., Gibaud, B., Grantcharov, T.P., Hashizume, M., Heckmann-Nötzel, D., Kenngott, H.G., Kikinis, R., Mündermann, L., Navab, N., Onogur, S., Roß, T., Sznitman, R., Taylor, R.H., Tizabi, M.D., Wagner, M., Hager, G.D., Neumuth, T., Padoy, N., Collins, J., Gockel, I., Goedeke, J., Hashimoto, D.A., Joyeux, L., Lam, K., Leff, D.R., Madani, A., Marcus, H.J., Meireles, O.R., Seitel, A., Teber, D., Ückert, F., Müller-Stich, B.P., Jannin, P., Speidel, S., 2022 · 2021
Later among the works it cites.
Videomoco: Contrastive video representation learning with temporally adversarial examples, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, Computer Vision Foundation / IEEE. pp. 11205–11214
Pan, T., Song, Y., Yang, T., Jiang, W., Liu, W., 2021 · 2021
Later among the works it cites.
Spatiotemporal contrastive video representation learning, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, Computer Vision Foundation / IEEE. pp. 6964–6974
Qian, R., Meng, T., Gong, B., Yang, M., Wang, H., Belongie, S.J., Cui, Y., 2021 · 2021
Later among the works it cites.
A kinematic bottleneck approach for pose regression of flexible surgical instruments directly from images
Sestini, L., Rosa, B., De Momi, E., Ferrigno, G., Padoy, N., 2021 · 2021
Later among the works it cites.
Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition
Shi, X., Jin, Y., Dou, Q., Heng, P., 2021 · 2021
Later among the works it cites.
Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part V, Springer. pp. 128–140
Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S., Verjans, J.W., Singh, R., Carneiro, G., 2021 · 2021
Later among the works it cites.
Wagner, M., Müller-Stich, B.P., Kisilenko, A., Tran, D., Heger, P., Mündermann, L., Lubotsky, D.M., Müller, B., Davitashvili, T., Capek, M., Reinke, A., Yu, T., Vardazaryan, A., Nwoye, C.I., Padoy, N., Liu, X., Lee, E., Disch, C., Meine, H., Xia, T., Jia, F., Kondo, S., Reiter, W., Jin, Y., Long, Y., Jiang, M., Dou, Q., Heng, P., Twick, I., Kirtaç, K., Hosgor, E., Bolmgren, J.L., Stenzel, M., von Siemens, B., Kenngott, H.G., Nickel, F., von Frankenberg, M., Mathis-Ullrich, F., Maier-Hein, L., Speidel, S., Bodenstedt, S., 2021 · 2021
Later among the works it cites.
Dense contrastive learning for self-supervised visual pre-training, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024–3033
Wang, X., Zhang, R., Shen, C., Kong, T., Li, L., 2021 · 2021
Later among the works it cites.
Federated contrastive learning for volumetric medical image segmentation, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part III, Springer. pp. 367–377
Wu, Y., Zeng, D., Wang, Z., Shi, Y., Hu, J., 2021 · 2021
Later among the works it cites.
Categorical relation-preserving contrastive knowledge distillation for medical image classification, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part V, Springer. pp. 163–173
Xing, X., Hou, Y., Li, H., Yuan, Y., Li, H., Meng, M.Q., 2021 · 2021
Later among the works it cites.
Real-time coarse-to-fine depth estimation on stereo endoscopic images with self-supervised learning, in: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 733–737
Yang, H., Kahrs, L.A., 2021 · 2021
Later among the works it cites.
Distinguishing differences matters: Focal contrastive network for peripheral anterior synechiae recognition, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part VIII, Springer. pp. 24–33
Yang, Y., Fang, H., Du, Q., Li, F., Zhang, X., Tan, M., Xu, Y., 2021 · 2021
Later among the works it cites.
Positional contrastive learning for volumetric medical image segmentation, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 221–230
Zeng, D., Wu, Y., Hu, X., Xu, X., Yuan, H., Huang, M., Zhuang, J., Hu, J., Shi, Y., 2021 · 2021
Later among the works it cites.
Unsupervised contrastive learning of radiomics and deep features for label-efficient tumor classification, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part II, Springer. pp. 252–261
Zhao, Z., Yang, G., 2021 · 2021
Later among the works it cites.
Anatomy-constrained contrastive learning for synthetic segmentation without ground-truth, in: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part I, Springer. pp. 47–56
Zhou, B., Liu, C., Duncan, J.S., 2021 · 2021
Later among the works it cites.
BEit: BERT pre-training of image transformers, in: International Conference on Learning Representations
Bao, H., Dong, L., Piao, S., Wei, F., 2022 · 2022
Closest in time.
Biomedical image analysis competitions: The state of current participation practice
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M.D., Isensee, F., Adler, T.J., Godau, P., Cheplygina, V., Kozubek, M., Ali, S., Gupta, A., Kybic, J., Noble, A., de Solórzano, C.O., Pachade, S., Petitjean, C., Sage, D., Wei, D., Wilden, E., Alapatt, D., Andrearczyk, V., Baid, U., Bakas, S., Balu, N., Bano, S., Bawa, V.S., Bernal, J., Bodenstedt, S., Casella, A., Choi, J., Commowick, O., Daum, M., Depeursinge, A., Dorent, R., Egger, J., Eichhorn, H., Engelhardt, S., Ganz, M., Girard, G., Hansen, L., Heinrich, M., Heller, N., Hering, A., Huaulmé, A., Kim, H., Landman, B., Li, H.B., Li, J., Ma, J., Martel, A., Martín-Isla, C., Menze, B., Nwoye, C.I., Oreiller, V., Padoy, N., Pati, S., Payette, K., Sudre, C., van Wijnen, K., Vardazaryan, A., Vercauteren, T., Wagner, M., Wang, C., Yap, M.H., Yu, Z., Yuan, C., Zenk, M., Zia, A., Zimmerer, D., Bao, R., Choi, C., Cohen, A., Dzyubachyk, O., Galdran, A., Gan, T., Guo, T., Gupta, P., Haithami, M., Ho, E., Jang, I., Li, Z., Luo, Z., Lux, F., Makrogiannis, S., Müller, D., Oh, Y.t., Pang, S., Pape, C., Polat, G., Reed, C.R., Ryu, K., Scherr, T., Thambawita, V., Wang, H., Wang, X., Xu, K., Yeh, H., Yeo, D., Yuan, Y., Zeng, Y., Zhao, X., Abbing, J., Adam, J., Adluru, N., Agethen, N., Ahmed, S., Khalil, Y.A., Alenyà, M., Alhoniemi, E., An, C., Anwar, T., Arega, T.W., Avisdris, N., Aydogan, D.B., Bai, Y., Calisto, M.B., Basaran, B.D., Beetz, M., Bian, C., Bian, H., Blansit, K., Bloch, L., Bohnsack, R., Bosticardo, S., Breen, J., Brudfors, M., Brüngel, R., Cabezas, M., Cacciola, A., Chen, Z., Chen, Y., Chen, D.T., Cho, M., Choi, M.K., Xie, C.X.C., Cobzas, D., Cohen-Adad, J., Acero, J.C., Das, S.K., de Oliveira, M., Deng, H., Dong, G., Doorenbos, L., Efird, C., Fan, D., Serj, M.F., Fenneteau, A., Fidon, L., Filipiak, P., Finzel, R., Freitas, N.R., Friedrich, C.M., Fulton, M., Gaida, F., Galati, F., Galazis, C., Gan, C.H., Gao, Z., Gao, S., Gazda, M., Gerats, B., Getty, N., Gibicar, A., Gifford, R., Gohil, S., Grammatikopoulou, M., Grzech, D., Güley, O., Günnemann, T., Guo, C., Guy, S., Ha, H., Han, L., Han, I.S., Hatamizadeh, A., He, T., Heo, J., Hitziger, S., Hong, S., Hong, S., Huang, R., Huang, Z., Huellebrand, M., Huschauer, S., Hussain, M., Inubushi, T., Polat, E.I., Jafaritadi, M., Jeong, S., Jian, B., Jiang, Y., Jiang, Z., Jin, Y., Joshi, S., Kadkhodamohammadi, A., Kamraoui, R.A., Kang, I., Kang, J., Karimi, D., Khademi, A., Khan, M.I., Khan, S.A., Khantwal, R., Kim, K.J., Kline, T., Kondo, S., Kontio, E., Krenzer, A., Kroviakov, A., Kuijf, H., Kumar, S., La Rosa, F., Lad, A., Lee, D., Lee, M., Lena, C., Li, H., Li, L., Li, X., Liao, F., Liao, K., Oliveira, A.L., Lin, C., Lin, S., Linardos, A., Linguraru, M.G., Liu, H., Liu, T., Liu, D., Liu, Y., Lourenço-Silva, J., Lu, J., Lu, J., Luengo, I., Lund, C.B., Luu, H.M., Lv, Y., Lv, Y., Macar, U., Maechler, L., L., S.M., Marshall, K., Mazher, M., McKinley, R., Medela, A., Meissen, F., Meng, M., Miller, D., Mirjahanmardi, S.H., Mishra, A., Mitha, S., Mohy-ud Din, H., Mok, T.C.W., Murugesan, G.K., Karthik, E.N., Nalawade, S., Nalepa, J., Naser, M., Nateghi, R., Naveed, H., Nguyen, Q.M., Quoc, C.N., Nichyporuk, B., Oliveira, B., Owen, D., Pal, J.B., Pan, J., Pan, W., Pang, W., Park, B., Pawar, V., Pawar, K., Peven, M., Philipp, L., Pieciak, T., Plotka, S., Plutat, M., Pourakpour, F., Preložnik, D., Punithakumar, K., Qayyum, A., Queirós, S., Rahmim, A., Razavi, S., Ren, J., Rezaei, M., Rico, J.A., Rieu, Z., Rink, M., Roth, J., Ruiz-Gonzalez, Y., Saeed, N., Saha, A., Salem, M., Sanchez-Matilla, R., Schilling, K., Shao, W., Shen, Z., Shi, R., Shi, P., Sobotka, D., Soulier, T., Fadida, B.S., Stoyanov, D., Mun, T.S.H., Sun, X., Tao, R., Thaler, F., Théberge, A., Thielke, F., Torres, H., Wahid, K.A., Wang, J., Wang, Y., Wang, W., Wang, X., Wen, J., Wen, N., Wodzinski, M., Wu, Y., Xia, F., Xiang, T., Xiaofei, C., Xu, L., Xue, T., Yang, Y., Yang, L., Yao, K., Yao, H., Yazdani, A., Yip, M., Yoo, H., Yousefirizi, F., Yu, S., Yu, L., Zamora, J., Zeineldin, R.A., Zeng, D., Zhang, J., Zhang, B., Zhang, J., Zhang, F., Zhang, H., Zhao, Z., Zhao, Z., Zhao, J., Zhao, C., Zheng, Q., Zhi, Y., Zhou, Z., Zou, B., Maier-Hein, K., Jäger, P.F., Kopp-Schneider, A., Maier-Hein, L., 2022 · 2022
Closest in time.
Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
Closest in time.
Federated cycling (fedcy): Semi-supervised federated learning of surgical phases
Kassem, H., Alapatt, D., Mascagni, P., Karargyris, A., Padoy, N., 2022 · 2022
Closest in time.
mc-beit: Multi-choice discretization for image BERT pre-training, in: Avidan, S., Brostow, G.J., Cissé, M., Farinella, G.M., Hassner, T. (Eds.), Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXX, Springer. pp. 231–246
Li, X., Ge, Y., Yi, K., Hu, Z., Shan, Y., Duan, L., 2022 · 2022
Closest in time.
Computer vision in surgery: from potential to clinical value
Mascagni, P., Alapatt, D., Sestini, L., Altieri, M.S., Madani, A., Watanabe, Y., Alseidi, A., Redan, J.A., Alfieri, S., Costamagna, G., et al., 2022 · 2022
Closest in time.
Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos
Nwoye, C.I., Yu, T., Gonzalez, C., Seeliger, B., Mascagni, P., Mutter, D., Marescaux, J., Padoy, N., 2022b · 2022
Closest in time.
Rivoir, D., Funke, I., Speidel, S., 2022 · 2022
Closest in time.
Masked feature prediction for self-supervised visual pre-training, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14668–14678
Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C., 2022 · 2022
Closest in time.
Simmim: A simple framework for masked image modeling, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H., 2022 · 2022
Closest in time.