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Found 12 papers, 3 papers with code
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite.
A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs
Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer.
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images.
NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose a simple CNN-based approach to speed up collecting annotations for these objects which requires minimum interaction from the annotator.
PanNuke Dataset Extension, Insights and Baselines
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data.
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.
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework
Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively.
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.
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades.
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.
Classification
Classification Of Breast Cancer Histology Images
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