[1] A.P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, N. Padoy. EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE Trans. on Medical Imaging 2016.
[2] Pytorch ResNet documentation - https://pytorch.org/hub/pytorch_vision_resnet/
[3] Nguyen Long, Lin Dongyun, Lin Zhiping, Cao Jiuwen - Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation
[4] Riaz Ullah Khan, Xiaosong Zhang, Rajesh Kumar, Emelia Opoku Aboagye - Evaluating the Performance of ResNet Model Based on Image Recognition ICCAI 2018
[5] Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaoou Tang, and Dahua Lin - Temporal Action Detection with Structured Segment Networks
[6] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton - ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
[7] Karen Simonyan, Andrew Zisserman - Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)
[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition (ResNet)
[9] DeepPhase: Surgical Phase Recognition in CATARACTS Videos - Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo, Petros Giataganas, Jean Nehme, Andre Chow, Danail Stoyanov
[10] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
[11] Gated Recurrent Unit (GRU) With PyTorch - https://blog.floydhub.com/gru-with-pytorch/
[12] Baseline code - https://github.com/JayJayBinks1/DeepPhase