Reading List.txt

Carreira, J., Caseiro, R., Batista, J., & Sminchisescu, C. (2012). Semantic segmentation with second-order pooling. In European conference on computer vision (pp. 430-443). Springer, Berlin, Heidelberg. PDF

Bao, J., Wu, Y., Chen, L., & Ji, X. (2018). Real-time object tracking based on deep learning. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-6). IEEE. PDF

Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017). ECO: Efficient Convolution Operators for Tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6638-6646). PDF

Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8971-8980). PDF

Held, D., Thrun, S., & Savarese, S. (2016). Learning to track at 100 FPS with deep regression networks. In European conference on computer vision (pp. 749-765). Springer, Cham. PDF

Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S. (2019). DeepSemantic Instance Segmentation of Surgical Instruments with Surgical Workflow Analysis. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3179-3185). IEEE. PDF

Zisimopoulos, O., Flouty, E., Luengo, I., & Stoyanov, D. (2019). DeepPhase: Surgical Phase Recognition in CATARACTS Videos. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. PDF

Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV) (pp. 801-818). PDF

Luo, X., Zhang, Z., Sun, Y., Chen, W., & Li, Y. (2020). End-to-end active object tracking and its real-world deployment via reinforcement learning. In European Conference on Computer Vision (pp. 475-490). Springer, Cham. PDF

Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., & Padoy, N. (2016). EndoNet: A deep architecture for recognition tasks on laparoscopic videos. IEEE transactions on medical imaging, 36(1), 86-97. PDF

Raza, S. H., Chevrier, A., Kanoulas, D., Stoyanov, D., & Sznitman, R. (2020). DeFT: Deformable fine-tuning network for semantic segmentation in surgical video. Medical image analysis, 65, 101783. PDF

García-Peraza-Herrera, L. C., Li, W., Gruijthuijsen, C., Devreker, A., Attilakos, G., Deprest, J., ... & Vercauteren, T. (2019). Real-time segmentation of non-rigid surgical tools based on deep learning and tracking. Computerized Medical Imaging and Graphics, 75, 34-46. PDF

Jiang, Y., Lu, Y., & Mao, J. (2019). Efficient Visual Tracking with Deep Reinforcement Learning. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1445-1449). IEEE. PDF

Zhang, Y., Guo, H., Fan, J., Lu, Y., & Wang, S. (2019). Deep semantic segmentation of natural and medical images: A review. Pattern Recognition, 88, 1-18. PDF

Shvets, A. A., Rakhlin, A., Kalinin, A. A., & Iglovikov, V. I. (2018). Automatic instrument segmentation in robot-assisted surgery using deep learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 624-628). IEEE. PDF

[2017TMI] EndoNet
[2018TMI] SV-RCNet
[2019MICCAI] Hard Frame Detection and Online Mapping for Surgical Phase Recognition
[2020MedIA] MTRCNet-CL
[2020MICCAI] TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks