scholarly journals Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

2020 ◽  
Vol 190 (10) ◽  
pp. 2111-2122
Author(s):  
Daniel Clymer ◽  
Stefan Kostadinov ◽  
Janet Catov ◽  
Lauren Skvarca ◽  
Liron Pantanowitz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Whole Slide Images

scholarly journals Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images

2019 ◽  
Vol 24 (12) ◽  
pp. 1 ◽  
Author(s):  
Lopamudra Mukherjee ◽  
Huu Dat Bui ◽  
Adib Keikhosravi ◽  
Agnes Loeffler ◽  
Kevin W. Eliceiri
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Whole Slide Images

scholarly journals Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

2014 ◽  
Author(s):  
Angel Cruz-Roa ◽  
Ajay Basavanhally ◽  
Fabio González ◽  
Hannah Gilmore ◽  
Michael Feldman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Automatic Detection ◽  
Whole Slide Images

scholarly journals Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8242 ◽  
Author(s):  
Péter Bándi ◽  
Maschenka Balkenhol ◽  
Bram van Ginneken ◽  
Jeroen van der Laak ◽  
Geert Litjens
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Tissue Segmentation ◽  
Traditional Methods ◽  
Tissue Samples ◽  
Multiple Networks ◽  
Tissue Sections ◽  
Medical Centers ◽  
Whole Slide Images

Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples—staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to three traditional methods on a range of resolution levels using Dice score and sensitivity. We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the eight unfamiliar images. A future study should test this network prospectively.

scholarly journals HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

2021 ◽  
Vol 68 ◽  
pp. 101890
Author(s):  
Mart van Rijthoven ◽  
Maschenka Balkenhol ◽  
Karina Siliņa ◽  
Jeroen van der Laak ◽  
Francesco Ciompi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Whole Slide Images
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