Fetching the paper…
Reading the bibliography…
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
2017 robotic instrument segmentation challenge
Allan, M., Shvets, A., Kurmann, T., Zhang, Z., Duggal, R., Su, Y.H., Rieke, N., Laina, I., Kalavakonda, N., Bodenstedt, S., et al., 2019 · 1902
Earlier work this paper cites.
High-resolution representations for labeling pixels and regions
Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., Wang, J., 2019 · 1904
Earlier work this paper cites.
On the variance of the adaptive learning rate and beyond
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J., 2019 · 1908
Earlier work this paper cites.
I-divergence geometry of probability distributions and minimization problems
Csiszár, I., 1975 · 1975
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., Schmidhuber, J., 1997 · 1997
Earlier work this paper cites.
2018 robotic scene segmentation challenge
Allan, M., Kondo, S., Bodenstedt, S., Leger, S., Kadkhodamohammadi, R., Luengo, I., Fuentes, F., Flouty, E., Mohammed, A., Pedersen, M., et al., 2020 · 2001
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.
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.
Combining embedded accelerometers with computer vision for recognizing food preparation activities, in: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 729–738
Stein, S., McKenna, S.J., 2013 · 2013
Earlier work this paper cites.
Jhu-isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling, in: MICCAI workshop: M2cai
Gao, Y., Vedula, S.S., Reiley, C.E., Ahmidi, N., Varadarajan, B., Lin, H.C., Tao, L., Zappella, L., Béjar, B., Yuh, D.D., et al., 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer. pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Earlier work this paper cites.
Deep residual learning for image recognition. corr abs/1512.03385 (2015)
He, K., Zhang, X., Ren, S., Sun, J., 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Earlier work this paper cites.
" xception: Deep learning with depthwise separable convolutions", arxiv preprint
Chollet, F., 2016 · 2016
Earlier work this paper cites.
Temporal convolutional networks: A unified approach to action segmentation, in: Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, Springer. pp. 47–54
Lea, C., Vidal, R., Reiter, A., Hager, G.D., 2016 · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I., Hutter, F., 2016 · 2016
Earlier work this paper cites.
Linknet: Exploiting encoder representations for efficient semantic segmentation, in: 2017 IEEE visual communications and image processing (VCIP), IEEE. pp. 1–4
Chaurasia, A., Culurciello, E., 2017 · 2017
Earlier work this paper cites.
Knowledge-based support for surgical workflow analysis and recognition
Dergachyova, O., 2017 · 2017
Earlier work this paper cites.
Toolnet: holistically-nested real-time segmentation of robotic surgical tools, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 5717–5722
Garcia-Peraza-Herrera, L.C., Li, W., Fidon, L., Gruijthuijsen, C., Devreker, A., Attilakos, G., Deprest, J., Vander Poorten, E., Stoyanov, D., Vercauteren, T., et al., 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.W., Heng, P.A., 2017 · 2017
Earlier work this paper cites.
The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al., 2017 · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S., 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I., Hutter, F., 2017 · 2017
Earlier work this paper cites.
Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, Springer. pp. 240–248
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M., 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
Cited alongside, same era.
Why rankings of biomedical image analysis competitions should be interpreted with care
Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A.P., Carass, A., et al., 2018 · 2018
Cited alongside, same era.
Automatic instrument segmentation in robot-assisted surgery using deep learning, in: 2018 17th IEEE international conference on machine learning and applications (ICMLA), IEEE. pp. 624–628
Shvets, A.A., Rakhlin, A., Kalinin, A.A., Iglovikov, V.I., 2018 · 2018
Relational graph learning on visual and kinematics embeddings for accurate gesture recognition in robotic surgery, in: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 13346–13353
Long, Y., Wu, J.Y., Lu, B., Jin, Y., Unberath, M., Liu, Y.H., Heng, P.A., Dou, Q., 2021 · 2021
Later among the works it cites.
Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation
Sahu, M., Mukhopadhyay, A., Zachow, S., 2021 · 2021
Later among the works it cites.
Efficientnetv2: Smaller models and faster training, in: International conference on machine learning, PMLR. pp. 10096–10106
Tan, M., Le, Q., 2021 · 2021
Later among the works it cites.
Ranger21: a synergistic deep learning optimizer
Wright, L., Demeure, N., 2021 · 2021
Later among the works it cites.
Segformer: Simple and efficient design for semantic segmentation with transformers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Unet++: A nested u-net architecture for medical image segmentation, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer. pp. 3–11
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J., 2018 · 2018
Cited alongside, same era.
Ms-tcn: Multi-stage temporal convolutional network for action segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3575–3584
Farha, Y.A., Gall, J., 2019 · 2019
Cited alongside, same era.
Slowfast networks for video recognition, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 6202–6211
Feichtenhofer, C., Fan, H., Malik, J., He, K., 2019 · 2019
Cited alongside, same era.
Learning where to look while tracking instruments in robot-assisted surgery, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 412–420
Islam, M., Li, Y., Ren, H., 2019 · 2019
Cited alongside, same era.
Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22, Springer. pp. 440–448
Jin, Y., Cheng, K., Dou, Q., Heng, P.A., 2019 · 2019
Cited alongside, same era.
A dvrk-based framework for surgical subtask automation
Nagy, T.D., Haidegger, T., 2019 · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks, in: International conference on machine learning, PMLR. pp. 6105–6114
Tan, M., Le, Q., 2019 · 2019
Cited alongside, same era.
Upsnet: A unified panoptic segmentation network, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8818–8826
Xiong, Y., Liao, R., Zhao, H., Hu, R., Bai, M., Yumer, E., Urtasun, R., 2019 · 2019
Cited alongside, same era.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P., 2021 · 2021
Later among the works it cites.
Asformer: Transformer for action segmentation
Yi, F., Wen, H., Jiang, T., 2021 · 2021
Later among the works it cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting, in: Proceedings of the AAAI conference on artificial intelligence, pp. 11106–11115
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W., 2021 · 2021
Later among the works it cites.
Masked-attention mask transformer for universal image segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1290–1299
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R., 2022 · 2022
Later among the works it cites.
Ssis-seg: Simulation-supervised image synthesis for surgical instrument segmentation
Colleoni, E., Psychogyios, D., Van Amsterdam, B., Vasconcelos, F., Stoyanov, D., 2022 · 2022
Later among the works it cites.
Patg: position-aware temporal graph networks for surgical phase recognition on laparoscopic videos
Kadkhodamohammadi, A., Luengo, I., Stoyanov, D., 2022 · 2022
Later among the works it cites.
Adaptive t-vmf dice loss for multi-class medical image segmentation
Kato, S., Hotta, K., 2022 · 2022
Later among the works it cites.
Bridge-prompt: Towards ordinal action understanding in instructional videos, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19880–19889
Li, M., Chen, L., Duan, Y., Hu, Z., Feng, J., Zhou, J., Lu, J., 2022 · 2022
Later among the works it cites.
Video swin transformer, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3202–3211
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H., 2022 · 2022
Later among the works it cites.
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., 2022 · 2022
Later among the works it cites.
Msdesis: Multitask stereo disparity estimation and surgical instrument segmentation
Psychogyios, D., Mazomenos, E., Vasconcelos, F., Stoyanov, D., 2022 · 2022
Later among the works it cites.
Robust deep learning-based semantic organ segmentation in hyperspectral images
Seidlitz, S., Sellner, J., Odenthal, J., Özdemir, B., Studier-Fischer, A., Knödler, S., Ayala, L., Adler, T.J., Kenngott, H.G., Tizabi, M., et al., 2022 · 2022
Later among the works it cites.
Towards holistic surgical scene understanding, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 442–452
Valderrama, N., Ruiz Puentes, P., Hernández, I., Ayobi, N., Verlyck, M., Santander, J., Caicedo, J., Fernández, N., Arbeláez, P., 2022 · 2022
Later among the works it cites.
Gesture recognition in robotic surgery with multimodal attention
Van Amsterdam, B., Funke, I., Edwards, E., Speidel, S., Collins, J., Sridhar, A., Kelly, J., Clarkson, M.J., Stoyanov, D., 2022 · 2022
Later among the works it cites.
Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 431–441
Wang, Y., Long, Y., Fan, S.H., Dou, Q., 2022 · 2022
Later among the works it cites.
Trasetr: track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery, in: 2022 International Conference on Robotics and Automation (ICRA), IEEE. pp. 11186–11193
Zhao, Z., Jin, Y., Heng, P.A., 2022 · 2022
Later among the works it cites.
Matis: Masked-attention transformers for surgical instrument segmentation, in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), IEEE
Ayobi, N., Pérez-Rondón, A., Rodríguez, S., Arbeláez, P., 2023 · 2023
Closest in time.
Lovit: Long video transformer for surgical phase recognition
Liu, Y., Boels, M., Garcia-Peraza-Herrera, L.C., Vercauteren, T., Dasgupta, P., Granados, A., Ourselin, S., 2023 · 2023
Closest in time.
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S.W., Khan, M.H., Hayat, M., Khan, F.S., Fu, H., 2023 · 2023
Closest in time.