Aboobacker, S.Verma, A.Vijayasenan, D.Sumam David, S.Suresh, P.K.Sreeram, S.2026-02-0620222022 National Conference on Communications, NCC 2022, 2022, Vol., , p. 82-87https://doi.org/10.1109/NCC55593.2022.9806747https://idr.nitk.ac.in/handle/123456789/29936Automation in the detection of malignancy in effusion cytology helps to save time and workload for cytopathologists. Cytopathologists typically consider a low-resolution image to identify the malignant regions. The identified regions are scanned at a higher resolution to confirm malignancy by investigating the cell level behaviour. Scanning and processing time can be saved by zooming only the identified malignant regions instead of entire low-resolution images. This work predicts malignancy in cytology images at a very low resolution (4X). Annotation of cytology images at a very low resolution is challenging due to the blurring of features such as nuclei and texture. We address this issue by upsampling the very low-resolution images using adversarial training. This work develops a semantic segmentation model trained on 10X images and reuse the network to utilize the 4X images. The prediction results of low resolution images improved by 15% in average F-score for adversarial based upsampling compared to a bicubic filter. The high resolution model gives a 95% average F-score for high resolution images. Also, the sub-area of the whole slide that requires to be scanned at high magnification is reduced by approximately 61% while using adversarial based upsampling compared to a bicubic filter. © 2022 IEEE.Deep Neural NetworkEffusion CytologyGANSemantic SegmentationSuper-Resolution