no code implementations • 1 Feb 2024 • Rebecca Pattichis, Marios S. Pattichis
There is strong interest in developing mathematical methods that can be used to understand complex neural networks used in image analysis.
no code implementations • 8 Dec 2023 • Marios S. Pattichis, Venkatesh Jatla, Alvaro E. Ullao Cerna
The paper provides a survey of the development of machine-learning techniques for video analysis.
no code implementations • 1 Jul 2022 • Luis Sanchez Tapia, Marios S. Pattichis, Sylvia Celedon-Pattichis, Carlos Lopez Leiva
Large-scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources.
no code implementations • 22 Dec 2021 • Wenjing Shi, Marios S. Pattichis, Sylvia Celedón-Pattichis, Carlos LópezLeiva
We introduce the problem of detecting a group of students from classroom videos.
no code implementations • 14 Oct 2021 • Wenjing Shi, Marios S. Pattichis, Sylvia Celedón-Pattichis, Carlos LópezLeiva
Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems.
no code implementations • 13 Oct 2021 • Sravani Teeparthi, Venkatesh Jatla, Marios S. Pattichis, Sylvia Celedon Pattichis, Carlos LopezLeiva
The detection results were improved to 81% by using our optimized approach for data augmentation.
1 code implementation • 11 Nov 2019 • Rogers F. Silva, Sergey M. Plis, Tulay Adali, Marios S. Pattichis, Vince D. Calhoun
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.
no code implementations • 26 Nov 2018 • Alvaro Ulloa, Linyuan Jing, Christopher W. Good, David P. vanMaanen, Sushravya Raghunath, Jonathan D Suever, Christopher D Nevius, Gregory J Wehner, Dustin Hartzel, Joseph B. Leader, Amro Alsaid, Aalpen A. Patel, H. Lester Kirchner, Marios S. Pattichis, Christopher M. Haggerty, Brandon K. Fornwalt
We show that a large dataset of 723, 754 clinically-acquired echocardiographic videos (~45 million images) linked to longitudinal follow-up data in 27, 028 patients can be used to train a deep neural network to predict 1-year mortality with good accuracy (area under the curve (AUC) in an independent test set = 0. 839).