scholarly journals Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

2019 ◽  
pp. 47-55 ◽  
Author(s):  
Amal Lahiani ◽  
Jacob Gildenblat ◽  
Irina Klaman ◽  
Shadi Albarqouni ◽  
Nassir Navab ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Pathology ◽  
Learning Approach ◽  
Unsupervised Deep Learning

scholarly journals Unsupervised Deep Learning via Affinity Diffusion

2020 ◽  
Vol 34 (07) ◽  
pp. 11029-11036
Author(s):  
Jiabo Huang ◽  
Qi Dong ◽  
Shaogang Gong ◽  
Xiatian Zhu
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
General Purpose ◽  
Training Data ◽  
Learning Approach ◽  
Model Learning ◽  
Feature Representations ◽  
Discriminative Feature ◽  
Training Samples ◽  
Unsupervised Deep Learning

Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet.

An unsupervised deep learning approach for 4DCT lung deformable image registration

2020 ◽  
Author(s):  
Yabo Fu ◽  
Yang Lei ◽  
Tonghe Wang ◽  
Kristin Higgins ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Learning Approach ◽  
Unsupervised Deep Learning
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