Faculty Publications

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Author: search.filters.author.Aboobacker, S.Subject: search.filters.subject.Supervised learning

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    WeakSegNet: Combining Unsupervised, Few-Shot, and Weakly Supervised Methods for the Semantic Segmentation of Low-Magnification Effusion Cytology Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.S.; Suresh, P.K.; Sreeram, S.
    Effusion cytology analysis can be time consuming for cytopathologists, but the burden can be reduced through automatic malignancy detection. The main challenge in the automation process is pixel-wise labeling. We proposed WeakSegNet, a new model that addresses the challenge of semantic segmentation in low-magnification images by utilizing only four images with pixel-wise labels. WeakSegNet combines unsupervised, few-shot, and weakly supervised learning methods. In the first stage, an unsupervised model, DeepClusterSeg, learns the homogeneous structures from different images. The few-shot method uses only four images with pixel-wise labels to map homogeneous structures to the required classes. The final stage utilized image-level labels to predict precise classes using weakly supervised learning. We conducted our experiments using a dataset from KMC Hospital, MAHE, which consisted of 345 images. We performed 5-fold cross-validation to evaluate the results. Our proposed model achieved promising results, with an F-score of 0.85 and an IoU of 0.81 for the malignant class, surpassing the performance of the standard k-means algorithm with weakly supervised learning (F-scores of 0.65 and an IoU of 0.61). The semantic segmentation of low-magnification images using our approach eliminated 47% of the sub-regions that need to be scanned at high magnification. This innovative approach reduces the workload of cytopathologists and maintains a high accuracy in effusion cytology malignancy detection. © 2013 IEEE.