HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
Medical Image Analysis, ISSN: 1361-8415, Vol: 68, Page: 101890
2021
- 123Citations
- 160Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations123
- Citation Indexes121
- 121
- Patent Family Citations2
- 2
- Captures160
- Readers160
- 160
Article Description
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. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. 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 segmentation. We have made HookNet publicly available by releasing the source code 1 1https://github.com/computationalpathologygroup/hooknet as well as in the form of web-based applications 2 2https://grand-challenge.org/algorithms/hooknet-breast/., 3 3https://grand-challenge.org/algorithms/hooknet-lung/. based on the grand-challenge.org platform.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1361841520302541; http://dx.doi.org/10.1016/j.media.2020.101890; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85096825471&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/33260110; https://linkinghub.elsevier.com/retrieve/pii/S1361841520302541; https://dx.doi.org/10.1016/j.media.2020.101890
Elsevier BV
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