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).
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.
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
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.
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.
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on Speech Enhancement on CHiME-3
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).
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).