129 Result(s)
within
English
Computer Imaging, Vision, Pattern Recognition and Graphics
Machine Learning
**ze Shen
Chapter
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Chapter and Conference Paper
A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation
An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diag...
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Chapter and Conference Paper
Progressive Deep Segmentation of Coronary Artery via Hierarchical Topology Learning
Coronary artery segmentation is a critical yet challenging step in coronary artery stenosis diagnosis. Most existing studies ignore important contextual anatomical information and vascular topologies, leading ...
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Chapter and Conference Paper
The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network
Automatic segmentation of head and neck (H&N) tumors plays an important and challenging role in clinical practice and radiomics researchers. In this paper, we developed an automated tumor segmentation method b...
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Chapter and Conference Paper
Local Graph Fusion of Multi-view MR Images for Knee Osteoarthritis Diagnosis
Magnetic resonance imaging (MRI) has become necessary in clinical diagnosis for knee osteoarthritis (OA), while deep neural networks can contribute to the computer-assisted diagnosis. Recent works prove that i...
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Chapter and Conference Paper
Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images
Kidney cancer is one of the top ten cancers in the world, and its incidence is still increasing. Early detection and accurate treatment are the most effective control methods. The precise and automatic segment...
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Chapter and Conference Paper
Point Beyond Class: A Benchmark for Weakly Semi-supervised Abnormality Localization in Chest X-Rays
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed by experienced radiologists, ...
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Chapter and Conference Paper
Learning Towards Synchronous Network Memorizability and Generalizability for Continual Segmentation Across Multiple Sites
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restricti...
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Chapter and Conference Paper
A Mixed-Factor Evolutionary Algorithm for Multi-objective Knapsack Problem
Nondominated-sorting plays an important role in multi-objective evolutionary algorithm in recent decades. However, it fails to work well when the target multi-objective problem has a complex Pareto front, brus...
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Chapter and Conference Paper
CAS-Net: Cross-View Aligned Segmentation by Graph Representation of Knees
Magnetic Resonance Imaging (MRI) has become an essential tool for clinical knee examinations. In clinical practice, knee scans are acquired from multiple views with stacked 2D slices, ensuring diagnosis accura...
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Chapter and Conference Paper
COVID-19 Pneumonia Classification with Transformer from Incomplete Modalities
COVID-19 is a viral disease that causes severe acute respiratory inflammation. Although with less death rate, its increasing infectivity rate, together with its acute symptoms and high number of infections, is...
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Chapter and Conference Paper
NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants
In addition to model accuracy, current neuroimaging studies require more explainable model outputs to relate brain development, degeneration, or disorders to uncover atypical local alterations. For this purpos...
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Chapter and Conference Paper
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based on severity, which can be au...
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Chapter and Conference Paper
HENet: Hierarchical Enhancement Network for Pulmonary Vessel Segmentation in Non-contrast CT Images
Pulmonary vessel segmentation in computerized tomography (CT) images is essential for pulmonary vascular disease and surgical navigation. However, the existing methods were generally designed for contrast-enha...
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Chapter and Conference Paper
Attentive Deep Canonical Correlation Analysis for Diagnosing Alzheimer’s Disease Using Multimodal Imaging Genetics
Integration of imaging genetics data provides unprecedented opportunities for revealing biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new model called attentive deep cano...
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Chapter and Conference Paper
Artifact Restoration in Histology Images with Diffusion Probabilistic Models
Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagn...
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Chapter and Conference Paper
Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation
Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared...
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Chapter and Conference Paper
Reliable Multimodality Eye Disease Screening via Mixture of Student’s t Distributions
Multimodality eye disease screening is crucial in ophthalmology as it integrates information from diverse sources to complement their respective performances. However, the existing methods are weak in assessin...
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Chapter and Conference Paper
Co-assistant Networks for Label Correction
The presence of corrupted labels is a common problem in the medical image datasets due to the difficulty of annotation. Meanwhile, corrupted labels might significantly deteriorate the performance of deep neura...
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Chapter and Conference Paper
Heterogeneous System Data Storage and Retrieval Scheme Based on Blockchain
In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and se...
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Chapter and Conference Paper
MoSID: Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation
Breast cancer is a major health issue, causing millions of deaths each year worldwide. Magnetic Resonance Imaging (MRI) is an effective tool for detecting and diagnosing breast tumors, with various MRI sequenc...
