12 citations · 17 across the 5 of their papers we have counts for
5 papers
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
Heyi Li, Dongdong Chen, William H. Nailon +2
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is at…
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
Heyi Li, Dongdong Chen, William H. Nailon +2
Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for breast cancer directly e…
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
Heyi Li, Dongdong Chen, William H. Nailon +2
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed gra…
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
Heyi Li, Dongdong Chen, William H. Nailon +2
We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image…
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
Heyi Li, Dongdong Chen, Bill Nailon +2
We explore the use of deep learning for breast mass segmentation in mammograms. By integrating the merits of residual learning and probabilistic graphical modelling with standard U…