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2022-03-23T09:04:27Z

Created page with "== Citation == Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-Electron..."

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== Citation ==

Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-Electron Microscopy. 2022. Proc. IEEE/CVF Winter Conference on Applications of Computer Vision, p. 3246-3255

== 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.

== Keywords ==

== Links ==

https://openaccess.thecvf.com/content/WACV2022/papers/Huang_Weakly_Supervised_Learning_for_Joint_Image_Denoising_and_Protein_Localization_WACV_2022_paper.pdf

== Related software ==

== Related methods ==

== Comments ==

2022Huang DenoisingAndPicking - Revision history

WikiSysop: Created page with "== Citation == Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-Electron..."