239 citations · 963 across the 124 of their papers we have counts for
12 papers · 1 filter
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
Yiyuan Yang, Riqiang Gao, Yucheng Tang +6
Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main…
Stochastic tissue window normalization of deep learning on computed tomography
Yuankai Huo, Yucheng Tang, Yunqiang Chen +8
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal C…
Contrast Phase Classification with a Generative Adversarial Network
Yucheng Tang, Ho Hin Lee, Yuchen Xu +10
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
Ho Hin Lee, Yucheng Tang, Olivia Tang +9
Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Riqiang Gao, Lingfeng Li, Yucheng Tang +6
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The reco…
Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection
Riqiang Gao, Yuankai Huo, Shunxing Bao +9
The field of lung nodule detection and cancer prediction has been rapidly developing with the support of large public data archives. Previous studies have largely focused on cross-…
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Camilo Bermudez, Justin Blaber, Samuel W. Remedios +6
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Rec…
Cortical Surface Parcellation using Spherical Convolutional Neural Networks
Prasanna Parvathaneni, Shunxing Bao, Vishwesh Nath +7
We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface re…
Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE
Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling +9
Intra-voxel models of the diffusion signal are essential for interpreting organization of the tissue environment at micrometer level with data at millimeter resolution. Recent adva…
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Yuankai Huo, Zhoubing Xu, Yunxi Xiong +7
Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging…
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
Jiachen Wang, Riqiang Gao, Yuankai Huo +6
Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer…
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Yunxi Xiong, Yuankai Huo, Jiachen Wang +3
Whole brain segmentation on structural magnetic resonance imaging (MRI) is essential for understanding neuroanatomical-functional relationships. Traditionally, multi-atlas segmenta…