AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries
This work addresses the need for more efficient cervical cancer screening methods in developing countries, though it appears incremental as it builds on existing AI and microscopy techniques.
The paper tackles the labor-intensive and error-prone process of cervical cancer screening in developing countries by introducing an AI-assisted system that integrates low-cost microscopes with efficient algorithms for automated whole-slide analysis, achieving enhanced accuracy and efficiency compared to state-of-the-art methods.
Cervical cancer remains a significant health challenge, with high incidence and mortality rates, particularly in transitioning countries. Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors, highlighting the need for more efficient screening methods. This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis. Our system uses a motorized microscope to capture cytology images, which are then processed through an AI pipeline involving image stitching, cell segmentation, and classification. We utilize the lightweight UNet-based model involving human-in-the-loop approach to train our segmentation model with minimal ROIs. CvT-based classification model, trained on the SIPaKMeD dataset, accurately categorizes five cell types. Our framework offers enhanced accuracy and efficiency in cervical cancer screening compared to various state-of-art methods, as demonstrated by different evaluation metrics.