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HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
[article]
2020
arXiv
pre-print
Preserved Fulltext
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. ...
Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image ...
The results shown in this paper are partly based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. ...
arXiv:2006.12230v1
fatcat:wtl232bvw5dbhm5wcfbfsnkela
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
2020
Medical Image Analysis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. ...
We show the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image ...
The results shown in this paper are partly based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga . ...
doi:10.1016/j.media.2020.101890
pmid:33260110
fatcat:a3t4bptdzjajdoxvnkql5db6gq
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
2022
Cancers
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis ...
In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. ...
[94] proposed HookNet, a semantic segmentation model combining context information in WSIs via multiple branches of encoder-decoder CNN, for tissue segmentation. ...
doi:10.3390/cancers14051199
pmid:35267505
pmcid:PMC8909166
fatcat:7tfcfh4z45goxbcgf23sncok5a
Built to last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology
[article]
2022
medRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
of computational pathology algorithms, we evaluated peer-reviewed articles available in Pubmed, published between January 2019 and March 2021, in five use cases: stain normalization, tissue type segmentation ...
We compiled criteria for data and code availability, and for statistical result analysis and assessed them in 161 publications. ...
Acknowledgment We thank Peter Schüffler (Munich) for inspiring feedback. S.J.W., L.L., and S.S. ...
doi:10.1101/2022.05.15.22275108
fatcat:wte5tu46gzf4pld25gbobqcjha