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Found 20 papers, 15 papers with code
AI-driven 3D Spatial Transcriptomics
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications.
Molecular-driven Foundation Model for Oncologic Pathology
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks.
Multimodal Whole Slide Foundation Model for Pathology
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL).
Multistain Pretraining for Slide Representation Learning in Pathology
Existing approaches for slide representation learning extend the principles of SSL from small images (e. g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide.
Multimodal Prototyping for cancer survival prediction
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification.
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity.
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
A potential early route towards clinical adoption for 3D pathology is to rely on pathologists for final diagnosis based on viewing familiar 2D H&E-like image sections from the 3D datasets.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL).
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Across three independent test datasets consisting of 1, 265 breast WSIs, 1, 946 lung WSIs, and 4, 584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines.
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.
A General-Purpose Self-Supervised Model for Computational Pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D).
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
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.
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.
Covariance-Free Sparse Bayesian Learning
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.
Gaussian Process Convolutional Dictionary Learning
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.
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on
Speech Enhancement
on CHiME-3
Fast Convolutional Dictionary Learning off the Grid
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).
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).
