William Hsu

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Professor

University of California, Los Angeles

United States

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About

William Hsu is a Professor at the University of California, Los Angeles, specializing in artificial intelligence applications in medical imaging and lung cancer screening. His recent research includes studies on mammography and lung cancer outcomes, as well as the development of algorithms for health data classification. He advocates for improved datasets and methodologies to enhance the performance of AI in healthcare settings.

Recent Grants

Grant: Open

Using artificial intelligence to support efficient same-day diagnostic imaging in breast cancer screening

Open Date: 2022-09-30

Close Date: 2027-09-29

Grant: Open

The Boston University - UCLA Lung Cancer Biomarker Characterization Center

Open Date: 2022-09-01

Close Date: 2027-07-31

Grant: Close

Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features

Open Date: 2022-09-01

Close Date: 2026-05-31

Grant: Close

SCH: INT: Personalized Real-Time Learning of Optimal Diagnostic Tests using Multi-Modal Clinical Data

Open Date: 2017-09-01

Close Date: 2021-08-31

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2022

Collaborators (5)

Diana Miglioretti

Professor and Division Chief of Biostatistics

University of California, Davis

UNITED STATES

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Robert Martí

Associate Professor.

Universitat de Girona

SPAIN

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Qiao Liu

Assistant Professor

Yale University

UNITED STATES

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Joann G. Elmore

University of California

UNITED STATES

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Adam Brentnall

Queen Mary University of London

UNITED KINGDOM

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William Hsu

Research Interests

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About

Recent Grants

Articles (19)

Influence of Mammography Acquisition Parameters on AI and Radiologist Interpretive Performance

Mammographic classification of interval breast cancers and artificial intelligence performance

Analyzing Patient Characteristics and Lung Cancer Outcomes Pre and Post the 2021 USPSTF Lung Cancer Screening Guidelines: Experience From a Large Academic Institution.

The Need for Contemporary, Diverse, and Annotated Datasets to Advance AI in Lung Cancer Screening

Evaluating an information theoretic approach for selecting multimodal data fusion methods

A phenotyping algorithm for classification of single ventricle physiology using electronic health records

Artificial Intelligence Is Brittle: We Need to Do Better

A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction

Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring

Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points

Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis

Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence

Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation

Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification

Collaborators (5)

Diana Miglioretti

Robert Martí

Qiao Liu

Joann G. Elmore

Adam Brentnall

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