HookNet-Lung
About
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/))
Interfaces
This algorithm implements all of the following input-output combinations:
Validation and Performance
Uses and Directions
This algorithm was developed for research purposes only.