Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images

Authors:
raw_author_name: Pradipta Sasmal
raw_affiliation_string: Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, Westbengal, 721302, India
raw_author_name: Vanshali Sharma
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India
raw_author_name: M.K. Bhuyan
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology Vellore, Katpadi, Vellore, Tamil Nadu, 632014, India
raw_author_name: Allam Jaya Prakash
raw_affiliation_string: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India
raw_author_name: Nagwan Abdel Samee
raw_affiliation_string: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
raw_author_name: Hayam Alamro
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
raw_author_name: Yuji Iwahori
raw_affiliation_string: Department of Computer Science and Engineering, Chubu University, Kasugai, Aichi, 487-8501, Japan
raw_author_name: Ryszard Tadeusiewicz
raw_affiliation_string: AGH University of Science and Technology, Department of Biocybernetics and Biomedical Engineering, Krakow, Poland
raw_author_name: U. Rajendra Acharya
raw_affiliation_string: School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
raw_author_name: Paweł Pławiak
raw_affiliation_string: Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland

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.