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Toward Robust Histology-Prior Embedding for Endomicroscopy Image Classification.
- Source :
-
IEEE Transactions on Medical Imaging . Nov2022, Vol. 41 Issue 11, p3242-3252. 11p. - Publication Year :
- 2022
-
Abstract
- Representation learning is the critical task for medical image analysis in computer-aided diagnosis. However, it is challenging to learn discriminative features due to the limited size of the dataset and the lack of labels. In this paper, we propose a stochastic routing normalization and neighborhood embedding framework with application to breast tissue classification by learning discriminative features of probe-based confocal laser endomicroscopy. In order to align the low-level and mid-level of pCLE and histology domain, we firstly build the domain-specific normalization module with stochastic activation strategy considering both depth-wise and feature-wise criterion. For high-level features, the latent centers are learned from the histology domain as the template for feature matching. The proposed method is evaluated on a clinical database with 700 pCLE mosaics. The accuracy of image classification with limited training samples demonstrates that the proposed method can outperform previous works on domain alignment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02780062
- Volume :
- 41
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 160651438
- Full Text :
- https://doi.org/10.1109/TMI.2022.3180340