Output Encoding by Compressed Sensing for Cell Detection with Deep Convnet

Output encoding often leads to superior accuracies in various machine learning tasks. In this paper we look at a significant task of cell detection/localization from microscopy images as a test case for output encoding. Since the output space is sparse for the cell detection problem (only a few pixel locations are cell centers), we employ compressed sensing (CS)-based output encoding here. Using random projections, CS converts the sparse, output pixel space into dense and short (i.e., compressed) vectors. As a regressor, we use deep convolutional neural net (CNN) to predict the compressed vectors. Then applying a $L_1$-norm recovery algorithm to the predicted vectors, we recover sparse cell locations in the output pixel space. We demonstrate CS-based output encoding provides us with the opportunity to do ensemble averaging to boost detection/localization scores. We experimentally demonstrate that the proposed CNN + CS framework (referred to as CNNCS) is competitive or better than the state-of-the-art methods on benchmark datasets for microscopy cell detection. In the AMIDA13 MICCAI grand competition, we achieve the 3rd highest F1-score in all the 17 participated teams.