44 citations · 85 across the 13 of their papers we have counts for
14 papers
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Andrew H. Song, Mane Williams, Drew F. K. Williamson +8
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selectin…
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
Iain Carmichael, Andrew H. Song, Richard J. Chen +3
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling com…
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Alexander Lin, Andrew H. Song, Berkin Bilgic +1
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-…
Adaptive State-Space Multitaper Spectral Estimation
Andrew H. Song, Seong-Eun Kim, Emery N. Brown
Short-time Fourier transform (STFT) is the most common window-based approach for analyzing the spectrotemporal dynamics of time series. To mitigate the effects of high variance on…
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
Alexander Lin, Andrew H. Song, Demba Ba
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures. Many of these networks require a large number of parameters and suffer fro…
Covariance-Free Sparse Bayesian Learning
Alexander Lin, Andrew H. Song, Berkin Bilgic +1
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms…
Gaussian Process Convolutional Dictionary Learning
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.…
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components
Andrew H. Song, Demba Ba, Emery N. Brown
To capture the slowly time-varying spectral content of real-world time-series, a common paradigm is to partition the data into approximately stationary intervals and perform infere…
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Bahareh Tolooshams, Ritwik Giri, Andrew H. Song +2
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary ma…
Fast Convolutional Dictionary Learning off the Grid
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)…
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams, Andrew H. Song, Simona Temereanca +1
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e.g., count data). The architectures are inspired by the problem of learn…
Spike Sorting by Convolutional Dictionary Learning
Andrew H. Song, Francisco Flores, Demba Ba
Spike sorting refers to the problem of assigning action potentials observed in extra-cellular recordings of neural activity to the neuron(s) from which they originate. We cast this…
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Leon Chlon, Andrew Song, Sandya Subramanian +4
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epoch…