Applying continual learning strategies for classification of cervical cells

Medical imaging plays an essential role in clinical diagnosis and therapy planning. Machine learning is gaining popularity because it captures illness and therapy response characteristics. The continual improvement of image collecting technology and diagnostic methods, the diversity of scanners and developing imaging protocols, and field shifts restrict the usefulness of machine learning as predicted accuracy on new data degrades or models become out-of-date. An example for such shifts is pap smear imaging. This thesis presents applications of continual learning strategies for tackling models' outdating due to the domain shifts in pap smear images. Two popular convolutiunal neural network (CNN) architectures (InceptionV3 and ResNet50), and a custom 2-layer CNN are trained with three pap smear benchmark datasets (Sipakmed, Herlev, and CRIC). Several continual learning strategies are employed when training the models. Finally, the models for each strategy are evaluated, and the comparative results are presented