← Back to work
Works cited by this work
35 works
Work: A framework utilizing deep learning techniques for the detection and classification of breast cancerous cells in mammographic images
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren +1
Article201661 citationsABIImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
Article201723 citationsABIMobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu +2
Preprint201817 citationsABIRethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe +2
Article201614 citationsABIInception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy, Sergey Ioffe, Sergey Ioffe +2
Article20178 citationsABIA review on image-based approaches for breast cancer detection, segmentation, and classification
Review article20213 citationsABIConvolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh, J. Shin, Suryakanth Gurudu +4
Article20163 citationsABITHE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Michael D. Heath, Kevin W. Bowyer, D B Kopans +1
Article20072 citationsABIThe Mammographic Image Analysis Society digital mammogram database
John Suckling, James Parker, Susan Astley +9
Article19942 citationsABIDeep learning in medical imaging and radiation therapy
Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski +5
Review article20182 citationsABI