Publications

Journal Articles

Deep Learning-Derived Myocardial Strain

Published in JACC: Cardiovascular Imaging, 2024

This study explores the application of deep learning techniques to derive myocardial strain measurements, aiming to enhance the assessment of cardiac function.

Recommended citation: Kwan AC, Chang EW, Jain I, Theurer J, Tang X, Francisco N, Haddad F, Liang D, Fábián A, Ferencz A, Yuan N, Merkely B, Siegel R, Cheng S, Kovács A, Tokodi M, Ouyang D. (2024). "Deep Learning-Derived Myocardial Strain." JACC: Cardiovascular Imaging, Published online March 12, 2024.
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Interpretable Deep Learning Prediction of 3D Assessment of Cardiac Function

Published in Pacific Symposium on Biocomputing, 2022

This study presents an interpretable deep learning approach for predicting 3D assessments of cardiac function, aiming to enhance understanding and accuracy in cardiac diagnostics.

Recommended citation: Duffy, G., Jain, I., He, B., & Ouyang, D. (2022). "Interpretable Deep Learning Prediction of 3D Assessment of Cardiac Function." Pacific Symposium on Biocomputing, 27, 231-241.
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Systematic Quantification of Sources of Variation in Ejection Fraction Calculation Using Deep Learning

Published in JACC: Cardiovascular Imaging, 2021

This study used deep learning to analyze the effects of common errors in echocardiographic tracing and view acquisition on LVEF measurements. Small variations, such as mistimed end-systole or mis-traced borders, caused significant changes in LVEF, often reclassifying patients and impacting clinical decisions. Automated AI methods could reduce this variability and improve diagnostic accuracy.

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Conference Papers

Hand Grip Pressure Visualization for Task Assistance

Published in IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2024, 2024

This paper presents a system that provides real-time visual feedback on hand grip pressure to assist users in adjusting their grasp during task performance.

Recommended citation: Sariya, A., Huard, A., Jain, I., Caetano, A., Höllerer, T., & Sra, M. (2024). "Hand Grip Pressure Visualization for Task Assistance." IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2024.
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An Adaptive Utilization of Convolutional Matrix Methods of Neuron Cell Segmentation with an Application Interface to Aid the Understanding of How Memory Recall Works

Published in IEEE Conference, 2020

This paper presents a method for denoising and segmenting hippocampal neuron images using convolutional matrix techniques, facilitating better analysis of neural stains and aiding in the understanding of memory recall processes.

Recommended citation: Author(s). (2020). "An Adaptive Utilization of Convolutional Matrix Methods of Neuron Cell Segmentation with an Application Interface to Aid the Understanding of How Memory Recall Works." IEEE Conference.
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Ishan Jain