Learning from crowds for automated histopathological image segmentation-Reference-Cited by-同舟云学术

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Learning from crowds for automated histopathological image segmentation

Published:2024-03 Issue: Volume:112 Page:102327
ISSN:0895-6111
Container-title:Computerized Medical Imaging and Graphics
language:en
Short-container-title:Computerized Medical Imaging and Graphics

Author:

López-Pérez Miguel^ORCID,Morales-Álvarez Pablo,Cooper Lee A.D.,Felicelli Christopher,Goldstein Jeffery,Vadasz Brian,Molina Rafael,Katsaggelos Aggelos K.

Funder

Ministerio de Ciencia e Innovación

Universidad de Granada

Junta de Andalucía

University of Granada Vice Chancellor's Office of Research and Knowledge Transfer

Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía

Federación Española de Enfermedades Raras

Publisher

Elsevier BV

Reference28 articles.

1. Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images;Aatresh;Comput. Med. Imaging Graph.,2021

2. AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images;Albarqouni;IEEE Trans. Med. Imaging,2016

3. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer;Amgad;GigaScience,2022

4. Structured crowdsourcing enables convolutional segmentation of histology images;Amgad;Bioinformatics,2019

5. Weakly supervised learning using attention gates for colon cancer histopathological image segmentation;Ben Hamida;Artif. Intell. Med.,2022

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