Conference Papers

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    Semi-supervised Semantic Segmentation of Effusion Cytology Images Using Adversarial Training
    (Springer Science and Business Media Deutschland GmbH, 2023) Rajpurohit, M.; Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.
    In pleural effusion, an excessive amount of fluid gets accumulated inside the pleural cavity along with signs of inflammation, infections, malignancies, etc. Usually, a manual cytological test is performed to detect and diagnose pleural effusion. The deep learning solutions for effusion cytology include a fully supervised model trained on effusion cytology images with the help of output maps. The low-resolution cytology images are harder to label and require the supervision of an expert, the labeling process time-consuming and expensive. Therefore, we have tried to use some portion of data without any labels for training our models using the proposed semi-supervised training methodology. In this paper, we proposed an adversarial network-based semi-supervised image segmentation approach to automate effusion cytology. The semi-supervised methodology with U-Net as the generator shows nearly 12% of absolute improvement in the f-score of benign class, 8% improvement in the f-score of malignant class, and 5% improvement in mIoU score as compared to a fully supervised U-Net model. With ResUNet++ as a generator, a similar improvement in the f-score of 1% for benign class, 8% for the malignant class, and 1% in the mIoU score is observed as compared to a fully supervised ResUNet++ model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Semi-supervised Semantic Segmentation for Effusion Cytology Images
    (Springer Science and Business Media Deutschland GmbH, 2023) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.
    Cytopathologists analyse images captured at different magnifications to detect the malignancies in effusions. They identify the malignant cell clusters from the lower magnification, and the identified area is zoomed in to study cell level details in high magnification. The automatic segmentation of low magnification images saves scanning time and storage requirements. This work predicts the malignancy in the effusion cytology images at low magnification levels such as 10 × and 4 ×. However, the biggest challenge is the difficulty in annotating the low magnification images, especially the 4 × data. We extend a semi-supervised learning (SSL) semantic model to train unlabelled 4 × data with the labelled 10 × data. The benign F-score on the predictions of 4 × data using the SSL model is improved 15% compared with the predictions of 4 × data on the semantic 10 × model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.