A survey on graph-based deep learning for computational histopathology

Author:

Ahmedt-Aristizabal David,Armin Mohammad Ali,Denman Simon,Fookes Clinton,Petersson Lars

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference153 articles.

1. Slic superpixels compared to state-of-the-art superpixel methods;Achanta;IEEE Trans. Pattern Anal. Mach. Intell,2012

2. Representation learning of histopathology images using graph neural networks;Adnan,2020

3. Graph-based deep learning for medical diagnosis and analysis: past, present and future;Ahmedt-Aristizabal;Sensors,2021

4. Amgad, M., Atteya, L.A., Hussein, H., Mohammed, K.H., Hafiz, E., Elsebaie, M.A., Alhusseiny, A.M., AlMoslemany, M.A., Elmatboly, A.M., Pappalardo, P.A., et al., 2021. Nucls: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation, arXiv:2102.09099.

5. Histographs: graphs in histopathology;Anand,2020

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