Transfer Learning for the Recognition of Immunogold Particles in TEM Imaging

We present a Transfer Learning (TL) framework based on Stacked Denoising Autoencoder (SDA) for the recognition of immunogold particles. These particles are part of a high-resolution method for the selective localization of biological molecules at the subcellular level only visible through Transmission Electron Microscopy (TEM). Four new datasets were acquired encompassing several thousands of immunogold particles. Due to the particles size (for a particular dataset a particle has a radius of 4 pixels in an image of size 4008×2670) the annotation of these datasets is extremely time taking. Thereby, we apply a TL approach by reusing the learning model that can be used on other datasets containing particles of different (or similar) sizes. In our experimental study we verified that our TL framework outperformed the baseline (not involving TL) approach by more than 20% of accuracy on the recognition of immunogold particles.