Search Results for author: Andrew H. Song

Found 20 papers, 15 papers with code

AI-driven 3D Spatial Transcriptomics

no code implementations25 Feb 2025 Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt, Ahrong Kim, Guillaume Jaume, Drew F. K. Williamson, Konstantin Hemker, Ming Y. Lu, Kritika Singh, Bowen Chen, Long Phi Le, Alexander S. Baras, Sizun Jiang, Ali Bashashati, Jonathan T. C. Liu, Faisal Mahmood

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

2 code implementations28 Jan 2025 Anurag Vaidya, Andrew Zhang, Guillaume Jaume, Andrew H. Song, Tong Ding, Sophia J. Wagner, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Perez, Richard J. Chen, Dina ElHarouni, Georges Ayoub, Connor Bossi, Keith L. Ligon, Georg Gerber, Long Phi Le, Faisal Mahmood

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.

Benchmarking Diagnostic +4

Multimodal Whole Slide Foundation Model for Pathology

2 code implementations29 Nov 2024 Tong Ding, Sophia J. Wagner, Andrew H. Song, Richard J. Chen, Ming Y. Lu, Andrew Zhang, Anurag J. Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, Drew F. K. Williamson, Bowen Chen, Cristina Almagro-Perez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihiro Kawabe, Shumpei Ishikawa, Georg Gerber, Tingying Peng, Long Phi Le, Faisal Mahmood

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).

Cross-Modal Retrieval model +4

Multistain Pretraining for Slide Representation Learning in Pathology

1 code implementation5 Aug 2024 Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood

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.

Representation Learning Self-Supervised Learning +1

Multimodal Prototyping for cancer survival prediction

1 code implementation28 Jun 2024 Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood

Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification.

Prediction Survival Prediction +1

Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments

1 code implementation11 Jun 2024 Gan Gao, Andrew H. Song, Fiona Wang, David Brenes, Rui Wang, Sarah S. L. Chow, Kevin W. Bishop, Lawrence D. True, Faisal Mahmood, Jonathan T. C. Liu

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.

Diagnostic Multiple Instance Learning

Transcriptomics-guided Slide Representation Learning in Computational Pathology

1 code implementation CVPR 2024 Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood

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.

Contrastive Learning Representation Learning +2

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.

Prognosis 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.

compressed sensing MRI Reconstruction +1

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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