no code implementations • 13 Dec 2023 • Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.
no code implementations • 29 Aug 2023 • Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
1 code implementation • 27 Jul 2023 • Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T. C. Liu, Alex Baras, Anil V. Parwani, Faisal Mahmood
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D).
1 code implementation • 17 Jun 2022 • Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment.
1 code implementation • 25 Feb 2022 • Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
1 code implementation • 10 Oct 2021 • Alexander Lin, Andrew H. Song, Demba Ba
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.
no code implementations • 21 May 2021 • Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.
no code implementations • 28 Mar 2021 • Andrew H. Song, Bahareh Tolooshams, Demba Ba
Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates.
1 code implementation • 30 Jan 2020 • Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on Speech Enhancement on CHiME-3
no code implementations • 22 Jul 2019 • Andrew H. Song, Francisco J. Flores, Demba Ba
Given a continuous-time signal that can be modeled as the superposition of localized, time-shifted events from multiple sources, the goal of Convolutional Dictionary Learning (CDL) is to identify the location of the events--by Convolutional Sparse Coding (CSC)--and learn the template for each source--by Convolutional Dictionary Update (CDU).
1 code implementation • ICML 2020 • Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba Ba
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).