[3dem] I2PC seminar: Next seminar will be about Cryo-forum

[JOSE LUIS VILAS PRIETO]

Dear community,

Following the tradition, I2PC (Instruct Image Processing Center) is  
organizing a seminar series on cryoEM and cryoET methods for image  
processing. This time the seminars and speakers will be announce in  
our webpage:

https://urldefense.com/v3/__https://i2pc.es/i2pc-seminar-series-2024/__;!!Mih3wA!H8pgphlO-_yCcJv42FUWnZG2hfAE-EGZdPfaFBV7mxLKTl97zwv18RGLh9RoOax-kHP_42jjtZpJs2KUMb8VqQ$ 

Our first seminar will be:

"CRYO-FORUM: A FRAMEWORK FOR ORIENTATION RECOVERY WITH UNCERTAINTY  
MEASURE WITH THE APPLICATION IN CRYO-EM IMAGE ANALYSIS" PRESENTED BY  
SZU-CHI CHUNG.

It will take place on OCTOBER 3RD AT 10:00 AM MADRID, PARIS, BERLIN  
TIME, 9:00 AM LONDON TIME

The seminar will be online and free to join in the next link:

https://urldefense.com/v3/__https://conectaha.csic.es/b/bla-rkh-dqa-rpn__;!!Mih3wA!H8pgphlO-_yCcJv42FUWnZG2hfAE-EGZdPfaFBV7mxLKTl97zwv18RGLh9RoOax-kHP_42jjtZpJs2JdG2G9Eg$ 

You can see an abstract here:

Cryo-electron microscopy (cryo-EM) shows great potential for  
determining protein 3D structures. However, the current workflow is  
slowed down by the complicated process of estimating 3D orientations  
from 2D projection images, especially as contaminated or low-quality  
images are challenging to identify in the dataset. Recent deep  
learning-based approaches aim to accelerate the process by employing  
amortized inference, eliminating the need for parameter estimation for  
each image. Despite this, these methods frequently overlook the  
presence of outliers and may not adequately concentrate on the  
components used within the network.

In this presentation, I will introduce a framework designed to recover  
orientations directly from the acquired 2D projections in an  
end-to-end manner. Our approach uses a 10-dimensional feature vector  
to represent an orientation, followed by a Quadratically-Constrained  
Quadratic Program to obtain the orientation prediction in unit  
quaternion format with uncertainty statistics. Furthermore,  
contrastive learning is employed to improve our model's generalization  
ability by incorporating pairwise distances between images. Finally,  
we also comprehensively evaluate the design choices in constructing  
the encoder network, a topic that has not received sufficient  
attention in the literature. Our numerical analysis demonstrates that  
our methodology effectively recovers orientations from 2D cryo-EM  
images in an end-to-end manner. Notably, the inclusion of uncertainty  
quantification allows for direct clean-up of the dataset at the 3D  
level.

Best regards

José Luis Vilas
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