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Found 11 papers, 5 papers with code
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).
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.
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival.
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method.
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity.
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them.
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images.
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Multi-tissue Nucleus Segmentation
on Kumar
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis.
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images.
