 | Yunliang CaiDepartment 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 |
| Concept | World rank |
|---|---|
| synchronized spectral network | #3 |
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Prominent publications by Yunliang Cai
Concussion classification via deep learning using whole-brain white matter fiber strains
[ PUBLICATION ]
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 |
Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter
[ PUBLICATION ]
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 |
Unsupervised Free-View Groupwise Segmentation for M3 Cardiac Images Using Synchronized Spectral Network
[ PUBLICATION ]
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 |
Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model
[ PUBLICATION ]
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 |
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 |
Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network
[ PUBLICATION ]
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 |
Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome
[ PUBLICATION ]
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 |
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 ...
Direct spondylolisthesis identification and measurement in MR/CT using detectors trained by articulated parameterized spine model
[ PUBLICATION ]
Automatic quantification of mammary glands on non-contrast X-ray CT by using a novel segmentation approach
[ PUBLICATION ]
