Classification of histopathological whole slide images based on multiple weighted semi-supervised domain adaptation

2022 ◽  
Vol 73 ◽  
pp. 103400
Author(s):  
Pin Wang ◽  
Pufei Li ◽  
Yongming Li ◽  
Jin Xu ◽  
Mingfeng Jiang
Keyword(s):  
Domain Adaptation ◽  
Whole Slide Images

Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images

2020 ◽  
Vol 58 (5) ◽  
pp. 3558-3573 ◽  
Author(s):  
Liang Yan ◽  
Bin Fan ◽  
Hongmin Liu ◽  
Chunlei Huo ◽  
Shiming Xiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Domain Adaptation ◽  
Remote Sensing Images

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

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