Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images
Authors:
- Pradipta Sasmal,
- Vanshali Sharma,
- M. K. Bhuyan,
- Allam Jaya Prakash,
- Kiran Kumar Patro,
- Nagwan Abdel Samee,
- Hayam Alamro,
- Yuji Iwahori,
- Ryszard Tadeusiewicz,
- U. Rajendra Acharya,
- Paweł Pławiak
Abstract
Early and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabelled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.
- Record ID
- CUT9b072c25484f46d8b91143d4514e8ccb
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2024
- Vol
- 658
- Pages
- [1-11]
- Article number
- 120033
- Other elements of collation
- fot.; rys.; schem.; tab.; Bibliografia (na s.) - 10-11; Bibliografia (liczba pozycji) - 50; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2023-12-29; Numeracja w czasopiśmie - Vol. 658
- Keywords in English
- colonoscopy images, colorectal polyps, generative adversarial network, histopathological images, semi-supervised learning
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.1016/j.ins.2023.120033 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0020025523016195 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 2
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9b072c25484f46d8b91143d4514e8ccb/
- URN
urn:pkr-prod:CUT9b072c25484f46d8b91143d4514e8ccb
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.