ConDANet: Contourlet Driven Attention Network for Automatic Nuclei Segmentation in Histopathology Images-Reference-Cited by-同舟云学术

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ConDANet: Contourlet Driven Attention Network for Automatic Nuclei Segmentation in Histopathology Images

Published:2023 Issue: Volume:11 Page:129321-129330
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Imtiaz Tamjid¹^ORCID,Fattah Shaikh Anowarul²^ORCID,Saquib Mohammad³^ORCID

Affiliation:

1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

2. Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

3. Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10271272.pdf?arnumber=10271272

Reference32 articles.

1. A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

2. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

3. Contextual Attention Network: Transformer Meets U-Net

4. Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement

5. Mask R-CNN

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1. Multi-scale Contourlet Knowledge Guide Learning Segmentation;IEEE Transactions on Multimedia;2023

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