[Reading] CNN Architectures in Medical Imaging Analysis

This is a blog post review on publications about Convolutional Neural Networks in healthcare and medical imaging.

Credits to Weina Jin

U-Net - Segmentation

1. Ronneberger, O., Fischer, P., & Brox, T. (2015). “U-Net: Convolutional Networks for Biomedical Image Segmentation”. ArXiv:1505.04597 [Cs]. Retrieved from http://arxiv.org/abs/1505.04597

V-Net - 3D volumetric segmentation

1. F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation”, arXiv:1606.04797 [cs], Jun. 2016.

3D U-Net

1. Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation”, arXiv:1606.06650 [cs], Jun. 2016.

Dermatology - Transfer learning

1. A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks”, Nature, vol. 542, no. 7639, pp. 115–118, 02 2017.

Histopathology - Color normalization - GAN

1. A. Bentaieb and G. Hamarneh, “Adversarial Stain Transfer for Histopathology Image Analysis”, IEEE Transactions on Medical Imaging, vol. 37, no. 3, pp. 792–802, Mar. 2018.

AlexNet

1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks”, Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017.

YOLO - Object detection

1. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, arXiv:1506.02640 [cs], Jun. 2015.

Super resolution

1. M. Haris, G. Shakhnarovich, and N. Ukita, “Deep Back-Projection Networks For Super-Resolution”, arXiv:1803.02735 [cs], Mar. 2018.

2. X. Wang, K. Yu, C. Dong, and C. C. Loy, “Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform”, arXiv:1804.02815 [cs], Apr. 2018.