Fetching the paper…
Reading the bibliography…
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.
L. Bottou, “Large-scale Machine Learning with Stochastic Gradient Descent,” pp. 177–186, 2010
2010
Earlier work this paper cites.
K. McGuinness and N. E. O’connor, “A Comparative Evaluation of Interactive Segmentation Algorithms,” Pattern Recognition , vol. 43, no. 2, pp. 434–444, 2010
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in NeurIPS , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward Embedded Detection of Polyps in WCE Images for Early Diagnosis of Colorectal Cancer,” International Journal of Computer Assisted Radiology and Surgery , vol. 9, no. 2, pp. 283–293, 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” in CVPR , 2015, pp. 815–823
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,” in NeurIPS , 2015, pp. 91–99
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” in CVPR , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in MICCAI . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-time Object Detection,” in CVPR , 2016, pp. 779–788
2016
Earlier work this paper cites.
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The Importance of Skip Connections in Biomedical Image Segmentation,” in Deep Learning and Data Labeling for Medical Applications . Springer, 2016, pp. 179–187
2016
Earlier work this paper cites.
S. Saxena and J. Verbeek, “Convolutional Neural Fabrics,” in NeurIPS , 2016, pp. 4053–4061
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Bernal, N. Tajkbaksh, F. J. Sánchez, B. J. Matuszewski, H. Chen, L. Yu, Q. Angermann, O. Romain, B. Rustad, I. Balasingham et al. , “Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge,” IEEE Transactions on Medical Imaging , vol. 36, no. 6, pp. 1231–1249, 2017
2017
Cited alongside, same era.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized Evolution for Image Classifier Architecture Search,” in AAAI , vol. 33, 2019, pp. 4780–4789
2019
Later among the works it cites.
X. Chen, L. Xie, J. Wu, and Q. Tian, “Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation,” in ICCV , 2019, pp. 1294–1303
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Zhang, Z. Qiu, J. Liu, T. Yao, D. Liu, and T. Mei, “Customizable Architecture Search for Semantic Segmentation,” in CVPR , 2019, pp. 11 641–11 650
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
F. Yu, D. Wang, E. Shelhamer, and T. Darrell, “Deep Layer Aggregation,” in CVPR , 2018, pp. 2403–2412
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
L.-C. Chen, M. Collins, Y. Zhu, G. Papandreou, B. Zoph, F. Schroff, H. Adam, and J. Shlens, “Searching for Efficient Multi-scale Architectures for Dense Image Prediction,” in NeurIPS , 2018, pp. 8699–8710
2018
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze and Excitation Networks,” in CVPR , 2018, pp. 7132–7141
2018
Cited alongside, same era.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,” in CVPR , 2018, pp. 6848–6856
2018
Cited alongside, same era.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Springer, 2018, pp. 3–11
2018
Cited alongside, same era.
2019
Later among the works it cites.
S. Kim, I. Kim, S. Lim, W. Baek, C. Kim, H. Cho, B. Yoon, and T. Kim, “Scalable Neural Architecture Search for 3D Medical Image Segmentation,” in MICCAI . Springer, 2019, pp. 220–228
2019
Later among the works it cites.
Z. Zhu, C. Liu, D. Yang, A. Yuille, and D. Xu, “V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation,” in 2019 International Conference on 3D Vision (3DV) . IEEE, 2019, pp. 240–248
2019
Later among the works it cites.
Y. Weng, T. Zhou, Y. Li, and X. Qiu, “NAS-UNet: Neural Architecture Search for Medical Image Segmentation,” IEEE Access , vol. 7, pp. 44 247–44 257, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
H.-Y. Zhou, S. Yu, C. Bian, Y. Hu, K. Ma, and Y. Zheng, “Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations,” in MICCAI . Springer, 2020
2020
Later among the works it cites.
H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu, “UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation,” in ICASSP . IEEE, 2020, pp. 1055–1059
2020
Later among the works it cites.
N. Ibtehaz and M. S. Rahman, “MultiResUNet: Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation,” Neural Networks , vol. 121, pp. 74–87, 2020
2020
Later among the works it cites.
Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A. L. Yuille, and D. Xu, “C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation,” in CVPR , 2020, pp. 4126–4135
2020
Later among the works it cites.