12 citations · 36 across the 8 of their papers we have counts for
8 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…
Fast and automated biomarker detection in breath samples with machine learning
Angelika Skarysz, Dahlia Salman, Michael Eddleston +7
Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatograp…
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…
Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion
Jonathan H. Mason, Alessandro Perelli, William H. Nailon +1
Scatter can account for large errors in cone-beam CT (CBCT) due to its wide field of view, and its complicated nature makes its compensation difficult. Iterative polyenergetic reco…
Polyquant CT: direct electron and mass density reconstruction from a single polyenergetic source
Jonathan H. Mason, Alessandro Perelli, William H. Nailon +1
Quantifying material mass and electron density from computed tomography (CT) reconstructions can be highly valuable in certain medical practices, such as radiation therapy planning…
Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?
Jonathan Mason, Alessandro Perelli, William Nailon +1
Due to its wide field of view, cone-beam computed tomography (CBCT) is plagued by large amounts of scatter, where attenuated photons hit the detector, and corrupt the linear models…