1 code implementation • 29 May 2021 • Heyi Li, Jinlong Liu, Xinyu Zhang, Yunzhi Bai, Huayan Wang, Klaus Mueller
But more importantly, the proposed $W$++ space achieves superior performance in both reconstruction quality and editing quality.
no code implementations • 29 Dec 2020 • Heyi Li, Dongdong Chen, William H. Nailon, Mike E. Davies, David Laurenson
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement.
no code implementations • 7 Aug 2020 • Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, David Laurenson
In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results.
no code implementations • 17 Jan 2020 • Heyi Li, Yuewei Lin, Klaus Mueller, Wei Xu
Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
no code implementations • 30 Jun 2019 • Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, David I. Laurenson
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses.
no code implementations • 1 Mar 2019 • Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, Dave Laurenson
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 processing.
no code implementations • 27 Aug 2018 • Heyi Li, Dong-Dong Chen, Bill Nailon, Mike Davies, Dave Laurenson
We explore the use of deep learning for breast mass segmentation in mammograms.
2 code implementations • 22 Dec 2017 • Heyi Li, Yunke Tian, Klaus Mueller, Xin Chen
In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein.