Multi-element microscope optimization by a learned sensing network with composite physical layers

TL;DR

This paper presents a learned sensing network that jointly optimizes microscope settings and classification algorithms, significantly improving automated malaria detection by enhancing image contrast and classification accuracy.

Contribution

It introduces a multi-element optimization approach combining programmable illumination and pupil transmission for better automated microscopy performance.

Findings

Learned sensing outperforms single-element setups.

Low-resolution images achieve classification accuracy comparable to high-resolution images.

Joint optimization improves automated detection of malaria parasites.

Abstract

Standard microscopes offer a variety of settings to help improve the visibility of different specimens to the end microscope user. Increasingly, however, digital microscopes are used to capture images for automated interpretation by computer algorithms (e.g., for feature classification, detection or segmentation), often without any human involvement. In this work, we investigate an approach to jointly optimize multiple microscope settings, together with a classification network, for improved performance with such automated tasks. We explore the interplay between optimization of programmable illumination and pupil transmission, using experimentally imaged blood smears for automated malaria parasite detection, to show that multi-element "learned sensing" outperforms its single-element counterpart. While not necessarily ideal for human interpretation, the network's resulting low-resolution microscope images (20X-comparable) offer a machine learning network sufficient contrast to match the classification performance of corresponding high-resolution imagery (100X-comparable), pointing a path towards accurate automation over large fields-of-view.