Deconvolving Convolutional Neural Network for Cell Detection
Shan E Ahmed Raza, Khalid AbdulJabbar, Mariam Jamal‐Hanjani, Selvaraju Veeriah, John P. C. Le Quesne, Charles Swanton, Yinyin Yuan · 2019
Automatic cell detection in histology images is a challenging task due to varying size, shape and features of cells and stain variations across large cohorts. Conventional deep learning methods regress the probability of each pixel belonging to the centre of a cell followed by detection of local maxima. We propose a three stage method (MapDe) to improve cell detection. (a) The dot annotations are convolved with a mapping filter to generate artificial labels. (b) A convolutional neural network (CNN) is modified to convolve its output with the same mapping filter. The mapping filter is fixed during training forcing the network to generate better probability maps. (c) Output of the trained CNN is deconvolved to generate points as cell detection. The results show that (1) local maxima performs better cell detection with probability maps generated using fixed convolution filter, (2) the results can be further improved by deconvolving the output with fewer parameters to tune.