Aboobacker, S.Vijayasenan, D.Sumam David, S.S.Suresh, P.K.Sreeram, S.2026-02-032025IEEE Access, 2025, 13, , pp. 144467-144478https://doi.org/10.1109/ACCESS.2025.3598953https://idr.nitk.ac.in/handle/123456789/20652Effusion 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.AutomationCellsDeep learningK-means clusteringLearning algorithmsLearning systemsPixelsSemantic SegmentationSemanticsSupervised learningUnsupervised learningDigital pathologiesF-scoreFew-shotHomogeneous structureMagnification imagesMalignancies detectionSemantic segmentationSupervised methodsWeakly supervised learningCytology