This chapter discusses a few applications of deep learning networks in cytopathology. Specifically, the detection of malaria from slide images of blood smear and classification of leukaemia cell-lines are addressed. The chapter starts with relevant theory for traditional (deep) multi-layer neural networks with back-propagation, followed by motivation, theory and training in Convolutional Neural Networks (CNN), the trending deep-learning based classifier. The detection of malaria from blood smear slide images using CNN is addressed followed by a discussion on the transfer learning capability of CNN by taking the classification of leukaemia cell-lines: K562, MOLT & HL60 as an example. The transfer learning capability of CNN is of particular interest especially when there are only very limited number of training samples to come up with a stand alone deep CNN classifier.
Gopakumar G and Subrahmanyam, G. R. K. Sai, “Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines”, in Handbook of Deep Learning Applications, V. Emilia Balas, Roy, S. Sekhar, Sharma, D., and Samui, P., Eds. Cham: Springer International Publishing, 2019, pp. 219–257.