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Nuclear segmentation and classification pipeline. Images are

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A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data - ScienceDirect

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

Tahsin Kurc's research works Stony Brook University, New York (Stony Brook) and other places

Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types

Lee COOPER, Assistant Professor, Ph.D., Biomedical Informatics / Biomedical Engineering

Our Guide to Effective Nuclei Segmentation

Sensors, Free Full-Text

Cell Nuclei Detection on Whole-Slide Histopathology Images Using HistomicsTK and Faster R-CNN Deep Learning Models

Frontiers A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images

Data, training and testing pipelines for cell segmentation. (a) A

Joel SALTZ, Professor and Chair; VP for Clinical Informatics; Associate Director Cancer Center, MD, PhD Duke University, Stony Brook University, New York, Stony Brook

Tony PAN, Doctor of Philosophy, Georgia Institute of Technology, Georgia, GT, School of Computational Science & Engineering

Nuclear segmentation and classification pipeline. Images are processed

Deep Learning in Histopathology (Part II), by Nishant Mishra

Our Guide to Effective Nuclei Segmentation

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