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Found 8 papers, 5 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.
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4.
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.
Towards a Visual-Language Foundation Model for Computational Pathology
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 diseases and patient cohorts.
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels.
