334 citations · 530 across the 29 of their papers we have counts for
31 papers
VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Luca L. Weishaupt, Simone de Brot, Javier Asin +9
Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human pathology largely unaddress…
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez +6
Spatial transcriptomics (ST) provides spatially resolved measurements of gene expression, enabling characterization of the molecular landscape of human tissue beyond histological a…
A Foundation Model for Spatial Proteomics
Muhammad Shaban, Yuzhou Chang, Huaying Qiu +57
Foundation models have begun to transform image analysis by acting as pretrained generalist backbones that can be adapted to many tasks even when post-training data are limited, ye…
Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Luca L. Weishaupt, Chengkuan Chen, Drew F. K. Williamson +8
Pathology is experiencing rapid digital transformation driven by whole-slide imaging and artificial intelligence (AI). While deep learning-based computational pathology has achieve…
AI-driven 3D Spatial Transcriptomics
Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt +13
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedi…
Accelerating Data Processing and Benchmarking of AI Models for Pathology
Andrew Zhang, Guillaume Jaume, Anurag Vaidya +2
Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly c…
Molecular-driven Foundation Model for Oncologic Pathology
Anurag Vaidya, Andrew Zhang, Guillaume Jaume +15
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognosti…
Multimodal Whole Slide Foundation Model for Pathology
Tong Ding, Sophia J. Wagner, Andrew H. Song +20
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transfe…
Multistain Pretraining for Slide Representation Learning in Pathology
Guillaume Jaume, Anurag Vaidya, Andrew Zhang +7
Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valua…
Multimodal Prototyping for cancer survival prediction
Andrew H. Song, Richard J. Chen, Guillaume Jaume +3
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratific…
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Guillaume Jaume, Paul Doucet, Andrew H. Song +8
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity. However, costs, rapidly evolving technology, and…
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Andrew H. Song, Richard J. Chen, Tong Ding +3
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide represent…
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya +5
Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g., 224x224 pixels), but scaling these models to learn slide embeddings…
Artificial Intelligence for Digital and Computational Pathology
Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson +4
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field…
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…
Towards a Visual-Language Foundation Model for Computational Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +10
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of di…
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
Guillaume Jaume, Anurag Vaidya, Richard Chen +3
Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis. However, this multimodal task is…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot +1
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelin…
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati +4
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate…
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Nadia Brancati, Anna Maria Anniciello, Pushpak Pati +10
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with lar…
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
Guillaume Jaume, Pushpak Pati, Valentin Anklin +2
Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relations…
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta +14
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entitie…