3.1k citations · 7.6k across the 74 of their papers we have counts for
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RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior
Ho Hin Lee, Yucheng Tang, Shunxing Bao +3
Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information. However, cur…
Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury
Cailey I. Kerley, Leon Y. Cai, Chang Yu +7
Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all…
Development and Characterization of a Chest CT Atlas
Kaiwen Xu, Riqiang Gao, Mirza S. Khan +8
A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complic…
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk
Riqiang Gao, Yucheng Tang, Kaiwen Xu +7
Clinical data elements (CDEs) (e.g., age, smoking history), blood markers and chest computed tomography (CT) structural features have been regarded as effective means for assessing…
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Yue Sun, Kun Gao, Zhengwang Wu +30
To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gr…
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
Mengyang Zhao, Aadarsh Jha, Quan Liu +6
Recently, single-stage embedding based deep learning algorithms gain increasing attention in cell segmentation and tracking. Compared with the traditional "segment-then-associate"…
CircleNet: Anchor-free Detection with Circle Representation
Haichun Yang, Ruining Deng, Yuzhe Lu +7
Object detection networks are powerful in computer vision, but not necessarily optimized for biomedical object detection. In this work, we propose CircleNet, a simple anchor-free d…
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng +7
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and t…
The Future of Digital Health with Federated Learning
Nicola Rieke, Jonny Hancox, Wenqi Li +14
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…
Semi-supervised Contrastive Learning Using Partial Label Information
Colin B. Hansen, Vishwesh Nath, Diego A. Mesa +3
In semi-supervised learning, information from unlabeled examples is used to improve the model learned from labeled examples. In some learning problems, partial label information ca…
Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI
Vishwesh Nath, Sudhir K. Pathak, Kurt G. Schilling +2
Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive approach for estimation of intra-voxel tissue microarchitecture and reconstruction of in vivo neural…
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…