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Found 6 papers, 2 papers with code
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns.
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers.
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance.
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.
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.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Ranked #2 on
Multi-tissue Nucleus Segmentation
on CoNSeP
