I DENTIFICATION OF C ERVICAL PATHOLOGY IN C OLPOSCOPY I MAGES USING A DVERSARIALLY T RAINED C ONVOLUTIONAL N EURAL N ETWORKS Abhilash Nandy Indian Institute of Technology Kharagpur West Bengal India raj12345@iitkgp.ac.in Rachana Sathish Indian Institute of Technology Kharagpur West Bengal India rachana.satish@iitkgp.ac.in Debdoot Sheet Indian Institute of Technology Kharagpur West Bengal India debdoot@ee.iitkgp.ac.in April 29, 2020 ABSTRACT Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries (LMICs) especially among the urban poor and slum dwellers. Several sophisticated techniques such as cytology tests, HPV tests etc. have been widely used for screening of cervical cancer. These tests are inherently time consuming. In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet which is trained in an adversarial fashion for classifying images of the cervix acquired using a colposcope. We validate performance on the Intel-Mobile ODT cervical image classification dataset. The proposed method outperforms the standard technique of fine-tuning convolutional neural networks pre-trained on ImageNet database with an average accuracy of 73.75% Keywords Cervical Cancer Screening · Adversarial Autoencoder · Convolutional Neural Network 1 Introduction Cervical cancer occurs when the epithelial cells lining up the cervix grow abnormally and invade neighboring tissues and organs of the body. Generally associated with infection of the human Papillomavirus (HPV) [1], associated with unhygienic sanitary conditions, cervical cancer accounts for the most common cause of cancer cases and deaths reported among urban poor in low and middle income countries (LMICs) [2]. Anatomically, the cervix is constituted of endocervix which is closest to the uterus and the part next to the vagina is the ectocervix. While endocervix is made up of columnar epithelium, the ectocervix is made up of stratified squamous epithelium cells and the boundary region between them called as transformation zone under pathological variation exhibits most of the origin of squamous cell carcinoma or dysplasia. Post its onset, the cancer can turn out to be completely ectocervical, partially ectocervical and partially endocervical, or completely endocervical. Since management of successive diagnostic, treatment and prognostic protocols are different for each of them [3], identification of their specific type is of immense importance. The clinical protocol makes use of an optical imaging device termed colposcope that provides magnified view of the cervix. This device is cost effective with low cost of ownership and commonly found across most primary healthcare centers in LMICs and rest of the world. The challenge however is lack of trained Gynecologists available at these arXiv:2004.13406v1 [eess.IV] 28 Apr 2020