Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

L. E. Hann, C. B. Winston, K. T. Brown, and T. Akhurst, ”Diagnostic imaging approaches and relationship to hepatobiliary cancer staging and therapy,” in Seminars in surgical oncology, 2000, pp. 94-115

2000

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, ”Imagenet: A large-scale hierarchical image database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009, pp. 248-255

2009

Earlier work this paper cites.

D. Smeets, D. Loeckx, B. Stijnen, B. De Dobbelaer, D. Vandermeulen, and P. Suetens, ”Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification,” Medical image analysis, vol. 14, pp. 13-20, 2010

2010

Earlier work this paper cites.

M. Schwier, J. H. Moltz, and H.-O. Peitgen, ”Object-based analysis of CT images for automatic detection and segmentation of hypodense liver lesions,” International journal of computer assisted radiology and surgery, vol. 6, p. 737, 2011

2011

Earlier work this paper cites.

V. Koltun, ”Efficient inference in fully connected crfs with gaussian edge potentials,” Adv. Neural Inf. Process. Syst, vol. 2, p. 4, 2011

2011

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ”Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097-1105

2012

Earlier work this paper cites.

B. Stewart and C. P. Wild, ”World cancer report 2014,” 2014

2014

Earlier work this paper cites.

K. Simonyan and A. Zisserman, ”Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014

Original

2014

Earlier work this paper cites.

K. Sahi, S. Jackson, E. Wiebe, G. Armstrong, S. Winters, R. Moore, et al., ”The value of “liver windows” settings in the detection of small renal cell carcinomas on unenhanced computed tomography,” Canadian Association of Radiologists Journal, vol. 65, pp. 71-76, 2014

2014

Cited alongside, same era.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, ”Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062, 2014

Original

2014

Cited alongside, same era.

J. Long, E. Shelhamer, and T. Darrell, ”Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431-3440

2015

Cited alongside, same era.

A. BenTaieb and G. Hamarneh, ”Topology Aware Fully Convolutional Networks for Histology Gland Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016, pp. 460-468

2016

A. Veit, M. J. Wilber, and S. Belongie, ”Residual networks behave like ensembles of relatively shallow networks,” in Advances in Neural Information Processing Systems, 2016, pp. 550-558

2016

Later among the works it cites.

A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, ”An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification,” IEEE Journal of Biomedical and Health Informatics, 2016

2016

Later among the works it cites.

Christ, Patrick Ferdinand, et al. ”Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2016

2016

Later among the works it cites.

L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, ”Semi-Automatic Skin Lesion Segmentation via Fully Convolutional Networks,” in ISBI, 2017

2017

Closest in time.