228 Result(s)
within
Chapter
**li Zhang
Artificial Intelligence
Health Informatics
Image Processing and Computer Vision
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Chapter and Conference Paper
Correction to: Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
The original version of this chapter was revised. An author’s name was misspelled. The name has been corrected to Alexis Dimitriadis.
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Chapter and Conference Paper
Structure-Aware Staging for Breast Cancer Metastases
Determining the stage of breast cancer metastases is an important component of cancer surveillance and control. It is laborious for pathologist to manually examine large amount of biological tissue and this pr...
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Chapter and Conference Paper
A Unified Mammogram Analysis Method via Hybrid Deep Supervision
Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system. In this work, we present a unified mammogram analysis framework for both whole-mamm...
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Chapter and Conference Paper
Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates
Automated pulmonary nodule detection plays an important role in lung cancer diagnosis. In this paper, we propose a pulmonary detection framework that can achieve high sensitivity with few candidates. First, th...
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Chapter and Conference Paper
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and pr...
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Chapter and Conference Paper
RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification meth...
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Chapter and Conference Paper
Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning
Identification of invasive cancer in Whole Slide Images (WSIs) is crucial for tumor staging as well as treatment planning. However, the precise manual delineation of tumor regions is challenging, tedious and t...
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Chapter and Conference Paper
MuTGAN: Simultaneous Segmentation and Quantification of Myocardial Infarction Without Contrast Agents via Joint Adversarial Learning
Simultaneous segmentation and full quantification (estimation of all diagnostic indices) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods s...
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Chapter and Conference Paper
Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
This paper presents a novel approach to modeling the posterior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric map** (LDDMM). We develop a Lapl...
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Chapter and Conference Paper
Local and Non-local Deep Feature Fusion for Malignancy Characterization of Hepatocellular Carcinoma
Deep feature derived from convolutional neural network (CNN) has demonstrated superior ability to characterize the biological aggressiveness of tumors, which is typically based on convolutional operations repe...
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Chapter and Conference Paper
Skin Lesion Classification in Dermoscopy Images Using Synergic Deep Learning
Automated skin lesion classification in the dermoscopy images is an essential way to improve diagnostic performance and reduce melanoma deaths. Although deep learning has shown proven advantages over tradition...
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Chapter and Conference Paper
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a la...
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Chapter and Conference Paper
Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network
The current standard photoacoustic (PA) technology is based on heavy, expensive and hazardous laser system for excitation of a tissue sample. As an alternative, light emitting diode (LED) offers safe, compact ...
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Chapter and Conference Paper
Single-Element Needle-Based Ultrasound Imaging of the Spine: An In Vivo Feasibility Study
Spinal interventional procedures, such as lumbar puncture, require insertion of an epidural needle through the spine without touching the surrounding bone structures. To minimize the number of insertion trials...
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Chapter and Conference Paper
Atlas Propagation Through Template Selection
Template-based atlas propagation can reduce registration cost in multi-atlas segmentation. In this method, atlases and testing images are registered to a common template. We show that using a common template m...
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Chapter and Conference Paper
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability...
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Chapter and Conference Paper
Robust Photoacoustic Beamforming Using Dense Convolutional Neural Networks
Photoacoustic (PA) is a promising technology for imaging of endogenous tissue chromophores and exogenous contrast agents in a wide range of clinical applications. The imaging technique is based on excitation o...
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Chapter and Conference Paper
Direct Reconstruction of Ultrasound Elastography Using an End-to-End Deep Neural Network
In this work, we developed an end-to-end convolutional neural network (CNN) to reconstruct the ultrasound elastography directly from radio frequency (RF) data. The novelty of this network is able to infer the ...
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Chapter and Conference Paper
Dual-Domain Cascaded Regression for Synthesizing 7T from 3T MRI
Due to the high cost and low accessibility of 7T magnetic resonance imaging (MRI) scanners, we propose a novel dual-domain cascaded regression framework to synthesize 7T images from the routine 3T images. Our ...
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Chapter and Conference Paper
Consistent Correspondence of Cone-Beam CT Images Using Volume Functional Maps
Dense correspondence between Cone-Beam CT (CBCT) images is desirable in clinical orthodontics for both intra-patient treatment evaluation and inter-patient statistical shape modeling and attribute transfer. Co...
