Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection

TL;DR

This paper introduces an end-to-end deep learning approach combining CNNs and compressed sensing for accurate microscopy cell detection, with a novel backpropagation rule for sparse coding layers, validated on benchmark datasets.

Contribution

The paper presents the first end-to-end training method combining CNNs with compressed sensing for cell detection, including a new backpropagation rule for sparse coding layers.

Findings

End-to-end training improves detection accuracy.

The method effectively recovers sparse cell locations.

Achieved excellent performance on benchmark datasets.

Abstract

Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural network (CNN) and compressed sensing (CS) or sparse coding (SC) for end-to-end training. We also derive, for the first time, a backpropagation rule, which is applicable to train any algorithm that implements a sparse code recovery layer. The key observation behind our algorithm is that cell detection task is a point object detection task in computer vision, where the cell centers (i.e., point objects) occupy only a tiny fraction of the total number of pixels in an image. Thus, we can apply compressed sensing (or, equivalently sparse coding) to compactly represent a variable number of cells in a projected space. Then, CNN regresses this compressed vector from the input microscopy image. Thanks to the SC/CS recovery algorithm (L1 optimization) that can recover sparse cell locations from the output of CNN. We train this entire processing pipeline end-to-end and demonstrate that end-to-end training provides accuracy improvements over a training paradigm that treats CNN and CS-recovery layers separately. Our algorithm design also takes into account a form of ensemble average of trained models naturally to further boost accuracy of cell detection. We have validated our algorithm on benchmark datasets and achieved excellent performances.