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Found 26 papers, 17 papers with code
Accelerating Data Processing and Benchmarking of AI Models for Pathology
Advances in foundation modeling have reshaped computational pathology.
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.
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.
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).
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).
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.
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
These pseudo labels are then used to train a node classification head for WSI segmentation.
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations.
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories.
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
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 relationship.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.
Ranked #33 on
Graph Classification
on MUTAG
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC).
