Presentations
Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound 2024
I presented my work in an oral and poster on concept-bottleneck models using existing radiological ontologies in the main session of MICCAI 2024.
Oral Poster Breast Cancer AI
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Customization of AI-Based Breast Cancer Risk Models for Validation in AANHPI Women 2024
My undergraduate mentee Cade Kane presented his work on the software customizations he made to adjust academic breast cancer risk models from mammography for use in the women in the HIPIMR at the SURE Symposium in Honolulu.
Oral Breast Cancer AI
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Application of AI to Breast Cancer for Hawaiʻi & the USAPI 2024
I was honored to have been selected as a Honolulu ARCS Scholar for 2024!
Oral Breast Cancer AI
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Fiducial Point Placement on Total-Body DXA Scans with Deep Learning 2024
I presented my work on the deep learning model I trained to place fiducial points on total-body DXA scans at the Biomedical Sciences & Health Disparities Symposium in Honolulu.
Oral DXA AI
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Is AI-enhanced breast ultrasound ready for breast cancer screening in low-resource environments? A systematic review 2024
I presented my systematic review on the current state of the literature for AI-enhanced breast ultrasound in low-resource and rural contexts at the 2024 AACR Annual Meeting.
Poster Breast Cancer AI
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Annotation Parsing in Clinical, Real-World Breast Ultrasound Imaging Data 2023
My undergraduate mentee Kailee Hung presented her work on the automatic pipeline she developed to parse sonographer annotations from clinical breast ultrasound data at the SURE Symposium in Honolulu.
Poster Breast Cancer AI
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Breast Cancer Stage Classification from Whole Slide Histology Images using a Pre-trained Framework 2023
I lead a team of UH graduate students (myself, Armin Soltan, and Zain Jabbar) in the NIH AIM-AHEAD Health Equity Data Challenge. Our solution placed third overall and we won $750! We also got the opportunity to present our work as a poster and as an oral during the AIM-AHEAD annual meeting in Bethesda, Maryland.
Poster Breast Cancer Oral AI
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Image-Based Models for Predicting Advanced Breast Cancer Risk 2023
I presented work done in conjunction with my labmate Dr. Lambert Leong on predicting advanced breast cancer from mammography imaging using deep radiomics at the International Breast Density Workshop in Kailua-Kona.
Poster Oral Breast Cancer AI
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Artificial Intelligence Predicts Mammographic Breast Density from Clinical Breast Ultrasound Images 2023
I presented my work on estimating BI-RADS mammographic breast density from breast ultrasound imaging at the International Breast Density Workshop in Kailua-Kona.
Poster Oral Breast Cancer AI
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Data Standardization of Clinical, Real-World Breast Ultrasound Imaging Data 2023
I presented my work on the automatic pipeline I developed to clean and standardize clinical breast ultrasound data at the Information and Computer Sciences Department Symposium in Honolulu. Pictured with me is my labmate Yannik Glaser! I was also awarded an outstanding graduate student award at this event.
Poster Breast Cancer AI
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Early Breast Cancer Diagnosis via Breast Ultrasound and Deep Learning 2023
I defended my Master's thesis in May 2023! Pictured is my committee (left to right): Dr. Peter Sadowski, myself, Dr. John Shepherd, Dr. Peter Washington.
Breast Cancer Oral AI
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Data Standardization of Clinical, Real-World Breast Ultrasound Imaging Data 2023
I presented my work on the automatic pipeline I developed to clean and standardize clinical breast ultrasound data at the Biomedical Sciences & Health Disparities Symposium in Honolulu.
Poster Breast Cancer AI
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The Effect of AI-Enhanced Breast Imaging on the Caring Radiologist-Patient Relationship 2023
I presented my work examining the care-ethical implications of AI integration into breast imaging at the 28th Pacific Biosymposium on Biocomputing on the Kohala Coast.
Oral Breast Cancer AI Care Ethics
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Artificial Intelligence Detects, Classifies, and Describes Lesions in Clinical Breast Ultrasound Images 2022
I presented my work on a Mask RCNN-based method for segmenting, classfying, and describing breast lesions in ultrasound according to the BI-RADS masses lexicon for ultrasound at the San Antonio Breast Cancer Symposium.
Poster Breast Cancer AI
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