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HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images [article]

Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, Jeroen van der Laak, Francesco Ciompi
2020 arXiv   pre-print
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

Mart van Rijthoven, Maschenka Balkenhol, Karina Silina, Jeroen van der Laak, Francesco Ciompi
2020 Medical Image Analysis  
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

Yawen Wu, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, Qi Zhu, Jie Zhang, Honghai Hong, Daoqiang Zhang, Kun Huang (+2 others)
2022 Cancers  
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]

Sophia J. Wagner, Christian Matek, Sayedali Shetab Boushehri, Melanie Boxberg, Lorenz Lamm, Ario Sadafi, Dominik J. E. Waibel, Carsten Marr, Tingying Peng
2022 medRxiv   pre-print
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