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.
CITATION STYLE
Gopakumar, G., & Sai Subrahmanyam, G. R. K. (2019). Deep learning applications to cytopathology: A study on the detection of malaria and on the classification of leukaemia cell-lines. In Smart Innovation, Systems and Technologies (Vol. 136, pp. 219–257). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-11479-4_11
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