Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-Electron Microscopy

Abstract

Deep learning-based object detection methods have shown promising results in various fields ranging from autonomous driving to video surveillance where input images have relatively high signal-to-noise ratios (SNR). On low SNR images such as biological electron microscopy (EM) data, however, the performance of these algorithms is significantly lower. Moreover, biological data typically lacks standardized annotations further complicating the training of detection algorithms. Accurate identification of proteins from EM images is a critical task, as the detected positions serve as inputs for the downstream 3D structure determination process. To overcome the low SNR and lack of image annotations, we propose a joint weakly-supervised learning framework that performs image denoising while detecting objects of interest. By leveraging per-pixel soft segmentation and consistency regularization, our framework denoises images without the need of clean images and is able to detect particles of interest even when less than 0.5% of the data are labeled. We validate our approach on real single-particle cryo-EM and cryo-electron tomography (ET) images which are known to suffer from extremely low SNR, and show that our strategy outperforms existing stateof-the-art (SofA) methods used in the cryo-EM field by a significant margin. We also evaluate the performance of our algorithm under decreasing SNR conditions and show that our method is more robust to noise than competing methods.