HookNet-Lung
About
Interfaces
This algorithm implements all of the following input-output combinations:
| Inputs | Outputs | |
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| 1 |
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Model Facts
Summary
HookNet is a multi-resolution semantic segmentation deep learning model that combines context and details via encoder-decoder branches. Results show that HookNet can simultaneously deal with subtle differences at high-resolution and contextual information. Furthermore, it has the potential to be helpful for any application where context and details are essential.
paper: https://www.sciencedirect.com/science/article/pii/S1361841520302541
code: https://github.com/DIAGNijmegen/pathology-hooknet
Mechanism
This algorithm has been developed for histopathology H&E stained lung slides. The algorithm expects two inputs: 1) the H&E slide, 2) a tissue-segmentation mask (which can be obtained via this algorithm (https://grand-challenge.org/algorithms/tissue-background-segmenation/))
Validation and Performance
Uses and Directions
This algorithm was developed for research purposes only.
Warnings
Common Error Messages
Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3