Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

TL;DR

This paper introduces a system combining stacked denoising autoencoders and transfer learning to detect and recognize immunogold particles in electron microscopy images, reducing manual labeling effort.

Contribution

It presents a novel approach using a LoG filter and transfer learning for immunogold particle recognition, specifically applied to maize cell analysis.

Findings

LoG detector achieved over 84% accuracy.

Transfer learning improved recognition accuracy by 10%.

System reduces manual annotation workload.

Abstract

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%.