(310) 794-3536
Office Info

924 Westwood Blvd, Suite 420, Room B

William Hsu, PhD

Associate Professor, Department of Radiological Sciences
Associate Professor, Department of Bioengineering
Member, Institute for Quantitative and Computational Biosciences
Member, Jonsson Comprehensive Cancer Center


I am Associate Professor in Residence in the Department of Radiological Sciences, Bioinformatics, and Bioengineering and a member of the Medical & Imaging Informatics group. I am also affiliated with the Institute of Quantitative and Computational Biosciences (QCB), UCLA Medical Informatics Home Area, and Jonsson Comprehensive Cancer Center. I actively collaborate with faculty members in the Center for Domain-Specific Computing, Clinical & Translational Science Institute, and UCLA-PKU Joint Research Institute. I currently serve as Chair of the AMIA Biomedical Imaging Informatics Working Group, a section co-editor for the IMIA Yearbook of Medical Informatics and a deputy editor for Radiology: Artificial Intelligence.

In the current data-rich healthcare environment, our capacity to collect vast amounts of longitudinal observational data needs to be matched with a comparable ability to continuously learn from the data and enable individually tailored medicine. My research focuses on the systematic integration of information across different data sources to improve the performance and robustness of clinical prediction models. I direct the Integrated Diagnostics Shared Resource, which is an interdepartmental resource that prospectively collects clinical, imaging, and molecular data to improve the detection and characterization of early-stage cancer. I also lead a team of postdoctoral fellows and graduate students who are developing computational tools that harness clinical, imaging, and molecular data to aid physicians with formulating timely, accurate, and personalized management strategies for individual patients. We adapt and validate novel artificial intelligence/machine learning algorithms, translating them into applications that enable precision medicine. My team works on problems related to data wrangling, knowledge representation, machine learning, and interpretation. We utilize a wide spectrum of approaches from statistical approaches to machine and reinforcement learning, depending on the problem at hand. I work closely with a team of software developers and analysts who harden and translate research products into real-world applications that improve the practice of radiology.

Reseach Interests

  • Develop machine learning approaches for discovering optimal care pathways for individuals
  • Build software tools and algorithms to enable integrated diagnostics research
  • Use deep neural networks to integrate multimodal data for integrated diagnostics
  • Improve methods for evaluating and adopting prediction models
  • Formalize the process of using published literature for treatment selection and experiment planning



Myint A, Corona E, Yang L, Nguyen BS, Lin C, Huang MZ, Shao P, Mwengela D, Didero M, Asokan I, Bui AAT, Hsu W, Maehara C, Naini BV, Kang Y, Bastani R, May FP. Gastroenterology visitation and reminders predict surveillance uptake for patients with adenomas with high-risk features. Sci Rep. 2021 Apr 22;11(1):8764. doi: 10.1038/s41598-021-88376-4. PMID: 33888839; PMCID: PMC8062682.


Wei L, Lin W, Hsu W. Using a generative adversarial network for CT normalization and its Impact on radiomic features. IEEE Intl Symp Biomedical Imaging. Iowa City, Iowa. Apr 3-7, 2020.
Smedley NF, El-Saden S, Hsu W. Discovering and interpreting transcriptomic drivers of imaging traits using neural networks. Bioinformatics. 2020 Feb 26:btaa126. DOI: 10.1093/bioinformatics/btaa126. Epub ahead of print. PMID: 32101278.
Li M, Hsu W, Xie X, Cong J, Gao W. SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising with Self-supervised Perceptual Loss Network. IEEE Trans Med Imaging. 2020 Jan 21. DOI: 10.1109/TMI.2020.2968472. Epub ahead of print. PMID: 31985412.
Lin Y, Wei L, Han SX, Aberle DR, Hsu W. EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141H. 16 March 2020. DOI: 10.1117/12.2551220.
Lin Y, Wei L, Han SX, Aberle DR, Hsu W. EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography. Proc SPIE Int Soc Opt Eng. 2020 Feb;11314:113141H. DOI: 10.1117/12.2551220. Epub 2020 Mar 16. PMID: 32606487; PMCID: PMC7325481.


Zhu B, Hsu W, Bui AAT. A Platform for Searching Clinical Documents and Medical Images to Facilitate AI/ML Development. Conference on Machine Intelligence in Medical Imaging 2019. Austin, Texas. September 22-23, 2019.
Johnson DC, Raman SS, Mirak SA, Kwan L, Bajgiran AM, Hsu W, Maehara CK, Ahuja P, Faiena I, Pooli A, Salmasi A, Sisk A, Felker ER, Lu DSK, Reiter RE. Detection of Individual Prostate Cancer Foci via Multiparametric Magnetic Resonance Imaging. Eur Urol. 2019 May;75(5):712-720. DOI: 10.1016/j.eururo.2018.11.031. Epub 2018 Dec 1. PMID: 30509763.
Petousis P, Han SX, Hsu W, Bui AAT. Generating Reward Functions Using IRL Towards Individualized Cancer Screening. Artif Intell Health (2018). 2019;11326:213-227. DOI: 10.1007/978-3-030-12738-1_16. Epub 2019 Feb 21. PMID: 31363717; PMCID: PMC6667225.
Omigbodun AO, Noo F, McNitt-Gray M, Hsu W, Hsieh SS. The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction. Med Phys. 2019 Oct;46(10):4563-4574. DOI: 10.1002/mp.13755. Epub 2019 Aug 27. PMID: 31396974.

Research Projects