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    • Yunliang Cai
    • Yunliang Cai: Influence Statistics

      Yunliang Cai

      Yunliang Cai

      Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America | Department of Biomedical Engineering, Worcester Polytechnic ...

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      Yunliang Cai:Expert Impact

      Concepts for whichYunliang Caihas direct influence:Deep learning,Injury prediction,Synchronized spectral network,Concussion classification,Shape discovery,Cardiac images,Segmentation approach,Automatic quantification.

      Yunliang Cai:KOL impact

      Concepts related to the work of other authors for whichfor which Yunliang Cai has influence:Deep learning,Medical images,Temporal horn,Brain strain,Lumbar spine,Intervertebral discs,Convolutional neural networks.

      KOL Resume for Yunliang Cai

      Year
      2018

      Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America

      2017

      Department of Biomedical Engineering, Worcester Polytechnic Institute, 01609, Worcester, MA, USA

      The Digital Imaging Group of London, Dept. of Medical Imaging, Western University, 268 Grosvenor St., SJHC, London, Ontario, N6A 4V2, Canada

      Thayer School of Engineering at Dartmouth (United States)

      2016

      Department of Biomedical Engineering, Worcester Polytechnic Institute, 01605, Worcester, Massachusetts, USA

      Western Univ. (Canada)

      2015

      Department of Medical Biophysics, University of Western Ontario, London, Canada, ON

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      Sample of concepts for which Yunliang Cai is among the top experts in the world.
      Concept World rank
      synchronized spectral network #3
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      Prominent publications by Yunliang Cai

      KOL-Index: 6050

      Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep ...

      Known for Deep Learning | White Matter | Brain Injury | Scalar Metrics | Concussion Prediction
      KOL-Index: 4887

      Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model-based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction ...

      Known for Injury Prediction | White Matter | Corpus Callosum | Superior Longitudinal Fasciculus | Single Training Dataset
      KOL-Index: 3763

      Image-based diagnosis and population study on cardiac problems require automatic segmentation on increasingly large amount of data from different protocols, different views, and different patients. However, current algorithms are often limited to regulated settings such as fixed view and single image from one specific modality, where the supervised learning methods can be easily employed but with restricted usability. In this paper, we propose the unsupervised free-view groupwise M3 ...

      Known for Cardiac Images | Synchronized Spectral Network | Groupwise Segmentation | Increasingly Large | Modality Chamber
      KOL-Index: 3722

      Computer-aided diagnosis of spine problems relies on the automatic identification of spine structures in images. The task of automatic vertebra recognition is to identify the global spine and local vertebra structural information such as spine shape, vertebra location and pose. Vertebra recognition is challenging due to the large appearance variations in different image modalities/views and the high geometric distortions in spine shape. Existing vertebra recognitions are usually ...

      Known for Vertebra Recognition | Spine Shape | Computeraided Diagnosis | Image Modalities | Ray Computed
      KOL-Index: 2908

      Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning ...

      Known for Feature Fusion | Learning Network | Image Modalities | Imaging Data | Spinal Clinical Diagnoses
      KOL-Index: 1957

      The diagnosis, comparative and population study of cardiac radiology data require heart segmentation on increasingly large amount of images from different modalities/chambers/patients under various imaging views. Most existing automatic cardiac segmentation methods are often limited to single image segmentation with regulated modality/region settings or well-cropped ROI areas, which is impossible for large datasets due to enormous device protocols and institutional differences. A pure ...

      Known for Spectral Network | Medical Images | Computed Algorithms | Segmentation Methods
      KOL-Index: 1056

      Reliable prediction of traumatic brain injury (TBI) outcome using neuroimaging is clinically important, yet, computationally challenging. To tackle this problem, we developed an injury prediction or classification pipeline based on diffusion tensor imaging (DTI) by combining a novel deep learning approach with statistical permutation tests. We first applied a multi-modal deep learning network to individually train a classification model for each DTI measure. Individual results were then ...

      Known for Deep Learning | Brain Injury | Diffusion Tensor Imaging
      KOL-Index: 188

      Unsupervised discovery and extraction of common shapes from unlabeled images is a fundamental problem in object recognition and has broad applications in practice. However, shape discovery suffers from the lack of consistent matching methods for finding the correspondences between objects with different colors/textures among the input images. In this paper, we propose a novel unsupervised shape discovery method using Synchronized Spectral Network (SSN) which provides automatic part-part ...

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      Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America | Department of Biomedical Engineering, Worcester Polytechnic Institute, 01609, Worcester, MA, USA | The Digital Imaging Group of London, D

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