Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images
Article
Sasmal, Pradipta, Sharma, Vanshali, Allam, Allam Jaya, Bhuyan, M.K., Patro, Kiran Kumar, Samee, Nagwan Abdel, Alamro, Hayam, Iwahori, Yuji, Tadeusiewicz, Ryszard, Acharya, U. Rajendra and Pławiak, Paweł. 2024. "Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images." Information Sciences. 658. https://doi.org/10.1016/j.ins.2023.120033
| Article Title | Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images |
|---|---|
| ERA Journal ID | 17908 |
| Article Category | Article |
| Authors | Sasmal, Pradipta, Sharma, Vanshali, Allam, Allam Jaya, Bhuyan, M.K., Patro, Kiran Kumar, Samee, Nagwan Abdel, Alamro, Hayam, Iwahori, Yuji, Tadeusiewicz, Ryszard, Acharya, U. Rajendra and Pławiak, Paweł |
| Journal Title | Information Sciences |
| Journal Citation | 658 |
| Article Number | 120033 |
| Number of Pages | 11 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0020-0255 |
| 1872-6291 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2023.120033 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0020025523016195 |
| 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 unlabeled 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. © 2023 Elsevier Inc. |
| Keywords | Colonoscopy images; Colorectal polyps; Generative adversarial network; Histopathological images; Semi-supervised learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Indian Institute of Technology Kharagpur, India |
| Indian Institute of Technology Guwahati, India | |
| Vellore Institute of Technology, India | |
| Aditya Institute of Technology and Management, India | |
| Princess Nourah bint Abdulrahman University, Egypt | |
| Chubu University, Japan | |
| AGH University of Science and Technology, Poland | |
| School of Mathematics, Physics and Computing | |
| Cracow University of Technology, Poland | |
| Polish Academy of Sciences, Poland |
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