Convolutional neural networks that teach microscopes how to image

TL;DR

This paper introduces a CNN-based approach that jointly optimizes microscope illumination and image classification, leading to improved accuracy in identifying malaria-infected cells.

Contribution

It presents a novel method integrating optical modeling into CNNs to optimize both imaging setup and classification in microscopy.

Findings

Achieved 5-10% higher accuracy in malaria cell detection.

Demonstrated joint optimization improves image classification performance.

Integrated optical model enhances CNN's ability to optimize physical imaging parameters.

Abstract

Deep learning algorithms offer a powerful means to automatically analyze the content of medical images. However, many biological samples of interest are primarily transparent to visible light and contain features that are difficult to resolve with a standard optical microscope. Here, we use a convolutional neural network (CNN) not only to classify images, but also to optimize the physical layout of the imaging device itself. We increase the classification accuracy of a microscope's recorded images by merging an optical model of image formation into the pipeline of a CNN. The resulting network simultaneously determines an ideal illumination arrangement to highlight important sample features during image acquisition, along with a set of convolutional weights to classify the detected images post-capture. We demonstrate our joint optimization technique with an experimental microscope configuration that automatically identifies malaria-infected cells with 5-10% higher accuracy than standard and alternative microscope lighting designs.