76 citations · 177 across the 12 of their papers we have counts for
12 papers
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation
Nishanjan Ravin, Sourajit Saha, Alan Schweitzer +4
We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the out-of-domain generalizability of a deep learning Tuberculosis screening algorithm. It is well known th…
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
Dakai Jin, Ziyue Xu, Youbao Tang +2
Data availability plays a critical role for the performance of deep learning systems. This challenge is especially acute within the medical image domain, particularly when patholog…
White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections
Dakai Jin, Ziyue Xu, Adam P. Harrison +1
Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders. Although…
Pathological Pulmonary Lobe Segmentation from CT Images using Progressive Holistically Nested Neural Networks and Random Walker
Kevin George, Adam P. Harrison, Dakai Jin +2
Automatic pathological pulmonary lobe segmentation(PPLS) enables regional analyses of lung disease, a clinically important capability. Due to often incomplete lobe boundaries, PPLS…
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
Adam P. Harrison, Ziyue Xu, Kevin George +3
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS…
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Mingchen Gao, Ziyue Xu, Le Lu +3
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually del…
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Mario Buty, Ziyue Xu, Mingchen Gao +3
Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves ex…
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao +6
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-d…
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative…
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Accurate delineation of pathological lungs from computed tomography (CT) images remains mostly unsolved because available methods fail to provide a reliable generic solution due to…
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Awais Mansoor, Ulas Bagci, Brent Foster +5
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide…
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems
Ulas Bagci, Jianhua Yao, Jesus Caban +3
Although radiologists can employ CAD systems to characterize malignancies, pulmonary fibrosis and other chronic diseases; the design of imaging techniques to quantify infectious di…