4 citations · 15 across the 7 of their papers we have counts for
8 papers
Equivariant Imaging: Learning Beyond the Range Space
Dongdong Chen, Julián Tachella, Mike E. Davies
In various imaging problems, we only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signa…
A Fully Convolutional Network for MR Fingerprinting
Dongdong Chen, Mohammad Golbabaee, Pedro A. Gomez +2
Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from h…
Deep Decomposition Learning for Inverse Imaging Problems
Dongdong Chen, Mike E. Davies
Deep learning is emerging as a new paradigm for solving inverse imaging problems. However, the deep learning methods often lack the assurance of traditional physics-based methods d…
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…
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Mohammad Golbabaee, Dongdong Chen, Pedro A. Gómez +2
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due…