Search Results for author: Andrew H. Song

Found 11 papers, 6 papers with code

Artificial Intelligence for Digital and Computational Pathology

no code implementations13 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.

whole slide images

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

1 code implementation17 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.

Survival Prediction whole slide images

Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning

1 code implementation10 Oct 2021 Alexander Lin, Andrew H. Song, Demba Ba

State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.

Clustering Deep Clustering +1

Covariance-Free Sparse Bayesian Learning

no code implementations21 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.

MRI Reconstruction Uncertainty Quantification

Gaussian Process Convolutional Dictionary Learning

no code implementations28 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.

Dictionary Learning Gaussian Processes

Fast Convolutional Dictionary Learning off the Grid

no code implementations22 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).

Dictionary Learning Spike Sorting

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