A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images-Reference-Cited by-同舟云学术

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Saednia Khadijeh¹,Tran William T.²,Sadeghi-Naini Ali¹

Affiliation:

1. York University,Department of Electrical Engineering and Computer Science,Toronto,ON,Canada

2. University of Toronto,Sunnybrook Health Sciences Centre,Department of Radiation Oncology and Biological Sciences Platform,Toronto,ON,Canada

Funder

NSERC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871996.pdf?arnumber=9871996

Reference25 articles.

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2. Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes

3. Breast Tumor Cell Nuclei Segmentation in Histopathology Images using EfficientUnet++ and Multi-organ Transfer Learning;dinh;Korea Multimedia Society Journal,2021

4. U-Net: Convolutional Networks for Biomedical Image Segmentation;ronneberger;Med Image Comput Comput Interv (MICCAI),2015

5. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Cell Tracking Challenge;IEEE,0

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