Aboobacker, ShajahanDeepu, VijayasenanSumam David S.2026-01-242024https://idr.nitk.ac.in/handle/123456789/18829This thesis focuses on developing an integrated system for automatically detecting malignancy in effusion cytology images. Effusion cytology plays a crucial role in diagnosing various diseases, including cancer, by analyzing the cells present in body fluids. This study aims to develop an integrated system that can handle different resolutions of images and accurately detect malignant cases. Effusion cytology greatly benefits from the automatic detection of malignant cells, providing significant assistance to cytopathologists. However, conventional automation algorithms often rely on high-magnification images for analysis. In contrast, cytopathologists consider multiple magnifications when evaluating cytology images for malignancy. Lower magnification images capture a larger area in a single frame compared to high magnification images. This allows cytopathologists to identify regions of interest (ROI) using textural and morphological characteristics of cell clusters. Once identified, the ROIs are examined at a higher magnification for a closer evaluation at the cell level. Initially, we trained the existing state-of-the-art models with high magnification images for the semantic segmentation and classification of effusion cytology images. We obtained state-of-the-art results for the semantic segmentation task with a mean F-score of 0.82 and classification performance with a sensitivity value of 1, specificity of 0.85, and an area under the curve (AUC) of 0.98. However, using lower magnification images can be beneficial in identifying malignant areas, as it reduces memory requirements and scanning time by focusing only on the ROI at higher magnification. However, detecting malignancy in low-magnification images (4X) is challenging due to the blurring of features such as texture and nuclei. This blurring also makes it difficult to label the images accurately at low magnification levels. Therefore, an alternative method is needed that doesn’t rely on labels for the lowest magnification. We propose two methods for the semantic segmentation of low-magnification images. The first method is based on semi-supervised learning, and the second uses a combination of unsupervised, few-shot and weakly supervised learning. In the semi-supervised approach, we have extended the MixMatch and Fixi Match algorithms from the classification task to semantic segmentation. We used augmentation of the images and reverse augmentation of the predicted label to achieve this. The proposed methods allow using the 4X images without any labels along with the labelled 10X images to train the semantic segmentation model. The average F-score of benign and malignant pixels on the predictions of 4X images using the Extended FixMatch and Extended MixMatch has improved approximately by 9% compared with the predictions of 4X data on the semantic 10X model. The Extended MixMatch reduces the area to be scanned at a higher magnification by approximately 62%. Only 38% of sub-regions of low-magnification images have to be scanned at a higher magnification, thereby saving scanning time. In the context of semi-supervised learning for semantic segmentation of low-magnification images, it is worth noting that while we can reduce the reliance on pixel-wise labels for 4X magnification data, we still require labelled data at a higher magnification level, specifically at 10X. We propose WeakSegNet, a novel approach that combines unsupervised, few-shot and weakly supervised methods for the semantic segmentation of low-magnification effusion cytology images. By leveraging image-level labels and a small number of images with pixel-wise labels, our model achieves accurate and efficient detection of malignancy. Our approach utilizes unlabeled low-magnification images for training, reducing the need for manual annotations. The significant elimination (approximately 47%) achieved by our model in higher magnification scanning demonstrates its potential for time and resource savings. Overall, our approaches offer an effective solution for automating malignancy detection in low-magnification images, improving efficiency in cytology analysis.enClassificationClusteringCytologyDigital PathologySemantic SegmentationSemi-Supervised LearningSupervised LearningUnsupervised LearningWeakly Supervised LearningThesis