Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization-Reference-Cited by-同舟云学术

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Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization

Published:2024-12 Issue: Volume:23 Page:669-678
ISSN:2001-0370
Container-title:Computational and Structural Biotechnology Journal
language:en
Short-container-title:Computational and Structural Biotechnology Journal

Author:

Mahbod Amirreza^ORCID,Dorffner Georg,Ellinger Isabella^ORCID,Woitek Ramona^ORCID,Hatamikia Sepideh

Funder

Österreichische Forschungsförderungsgesellschaft

Nvidia

Publisher

Elsevier BV

Reference59 articles.

1. Medical image analysis using convolutional neural networks: a review;Anwar;J Med Syst,2018

2. BACH: grand challenge on breast cancer histology images;Aresta;Med Image Anal,2019

3. Improving mask R-CNN for nuclei instance segmentation in hematoxylin & eosin-stained histological images;Bancher,2021

4. A survey on recent trends in deep learning for nucleus segmentation from histopathology images;Basu;Evolv Syst,2023

5. Instance segmentation of neuronal nuclei leveraging domain adaptation;Brady,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images;Scientific Data;2024-03-14

2. FUSeg: The Foot Ulcer Segmentation Challenge;Information;2024-03-01

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