Weakly Supervised Deep Learning for Detecting and Counting Dead Cells in Microscopy Images

Abstract

Counting dead cells is a key step in evaluating the performance of chemotherapy treatment and drug screening. Deep convolutional neural networks (CNNs) can learn complex visual features, but require massive ground truth annotations which is expensive in biomedical experiments. Counting cells, especially dead cells, with very few ground truth annotations remains unexplored. In this paper, we automate dead cell counting using a weakly supervised strategy. We took advantage of the fact that cell death is low before chemotherapy treatment and increases after treatment. Motivated by the contrast, we first design image level supervised only classification neural networks to detect dead cells. Based on the class response map in classification networks, we calculate a Dead Confidence Map (DCM) to specify confidence of each dead cell. Associated with peak clustering, local maximums in the DCM are used to count the number of dead cells. In addition, a biological experiment based weakly supervised data preparation strategy is proposed to minimize human intervention. We show classification performance compared to general purpose and cell classification networks, and report results for the image-level supervised counting task.