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 annotations, we propose a joint weakly-supervised learning framework that performs image denoising while detecting objects of interest. 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 three extremely low SNR cryo-EM datasets and show that our strategy outperforms existing state-of-the-art (SofA) methods used in the cryo-EM field by a significant margin.