Fine-grained breast cancer classification with bilinear convolutional neural networks (BCNNS)
- Liu, Weihuang College of Science, Harbin Institute of Technology, Shenzhen, China
- Juhas, Mario Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
- Zhang, Yang College of Science, Harbin Institute of Technology, Shenzhen, China
- 04.09.2020
Published in:
- Frontiers in Genetics. - 2020, vol. 11, p. 547327
English
Classification of histopathological images of cancer is challenging even for well- trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine- grained classification, respectively.
- Faculty
- Faculté des sciences et de médecine
- Department
- Médecine 3ème année
- Language
-
- English
- Classification
- Biological sciences
- License
- License undefined
- Identifiers
-
- RERO DOC 329634
- DOI 10.3389/fgene.2020.547327
- Persistent URL
- https://folia.unifr.ch/unifr/documents/309020
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