About Me
I am a Ph.D. candidate in Electrical Engineering at The University of Texas at Austin advised by Atlas Wang. As part of the Visual Informatics @ UT Austin (VITA) group, I develop deep learning and computer vision techniques to solve clinical decision-making and medical image analysis problems. My research is funded by the NSF Graduate Research Fellowship Program (GRFP). [CV]
Previously, I have worked with the Medical Imaging and Data Integration (MIDI) Lab at Michigan State University under Adam Alessio on multimodal learning for breast cancer prediction and pediatric rib fracture detection.
Interests
- Medical image analysis
- Computer-aided diagnosis
- Multimodal learning
- Self-supervised learning
- Long-tailed learning
Education
Ph.D. in Electrical Engineering, 2021-present
The University of Texas at Austin
M.S.E. in Electrical Engineering, 2021-2024
The University of Texas at Austin
B.A. in Mathematics, 2016-2020
Kenyon College
News
- [Jul. 2024] Paper accepted to npj Digital Medicine on deep survival analysis from longitudinal medical imaging
- [Jun. 2024] Paper accepted to Radiology: Artificial Intelligence on contrastive learning for improved fairness in chest X-ray classification
- [May 2024] Paper accepted to Communications Medicine on contrastive learning for echocardiography
- [May 2024] Paper accepted to Medical Image Analysis summarizing the CXR-LT challenge
- [Apr. 2024] Paper accepted to Scientific Reports on pediatric rib fracture detection
- [Mar. 2024] Our challenge, CXR-LT, was accepted to MICCAI 2024
- [Feb. 2024] Paper accepted to JAMA Cardiology on detecting aortic stenosis progression with deep learning [Preprint]
- [Jan. 2024] Paper accepted to JAMIA on self-supervised learning for electrocardiographic (ECG) images
- [Aug. 2023] Paper accepted to Nature Communications on fairness in chest X-ray classification
- [Jun. 2023] Paper accepted to European Heart Journal on severe aortic stenosis detection!
- [May 2023] Paper early accepted (top 14%) to MICCAI 2023 on pruning chest X-ray classifiers!
- [Mar. 2023] Our workshop, Computer Vision for Automated Medical Diagnosis (CVAMD), was accepted to ICCV 2023
- [Mar. 2023] 🎉 I was awarded the NSF Graduate Research Fellowship (GRFP)!! 🎉
- [Jan. 2023] Paper accepted to ISBI 2023 on multimodal fusion of histopathology imaging and clinical data from my internship with Artera!
- [Oct. 2022] Paper accepted to IEEE Transactions on Medical Imaging on weakly supervised localization in chest X-rays! [Link] [arXiv]
- [Aug. 2022] Preprint out on automated detection of severe aortic stenosis [Paper]
- [Jul. 2022] Paper accepted to DALI 2022 (MICCAI 2022 workshop) and abstract accepted to RSNA 2022 on long-tailed learning for chest X-rays
- [Jun. 2022] Paper accepted to IMLH 2022 (ICML 2022 workshop) on self-supervised learning of echocardiogram videos [Paper] [Code] [Poster]
- [May 2022] Started my internship with the AI team at Artera
- [Oct. 2021] One paper accepted to SPIE Medical Imaging 2022 on pediatric rib fracture detection
- [Aug. 2021] Started my Ph.D. at UT Austin with Atlas Wang!
- [Aug. 2021] One paper accepted to CVAMD 2021 (ICCV 2021 workshop) on multimodal learning
Recent Publications
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Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Communications Medicine
EchoCLR, a label-efficient self-supervised learning method for echocardiography.
EchoCLR, a label-efficient self-supervised learning method for echocardiography.
Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study
medRxiv preprint
Automated cardiomyopathy detection in point-of-care ultrasonography (POCUS).
Automated cardiomyopathy detection in point-of-care ultrasonography (POCUS).
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Medical Image Analysis
Overview of the CXR-LT challenge on long-tailed, multi-label disease classification from chest X-ray.
Overview of the CXR-LT challenge on long-tailed, multi-label disease classification from chest X-ray.
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
arXiv preprint
Automated eye disease prognosis by survival analysis from longitudinal medical imaging.
Automated eye disease prognosis by survival analysis from longitudinal medical imaging.
Contact
- gholste@utexas.edu