WikiSysop: Created page with "== Citation == Chen, J., Leung, V.C., Wang, R., Bubeck, D. and Dragotti, P.L. 2026. Masked Projection Modelling for Sparse-view cryo-EM Reconstruction. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2026), 11567–11571. == Abstract == Resolving conformational heterogeneity in cryo-electron microscopy (cryo-EM) remains challenging, especially for rare states. Standard reconstruction methods, reliant on abundant simi..."

2026-04-28T06:41:51Z

Created page with "== Citation == Chen, J., Leung, V.C., Wang, R., Bubeck, D. and Dragotti, P.L. 2026. Masked Projection Modelling for Sparse-view cryo-EM Reconstruction. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2026), 11567–11571. == Abstract == Resolving conformational heterogeneity in cryo-electron microscopy (cryo-EM) remains challenging, especially for rare states. Standard reconstruction methods, reliant on abundant simi..."

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== Citation ==

Chen, J., Leung, V.C., Wang, R., Bubeck, D. and Dragotti, P.L. 2026. Masked Projection Modelling for Sparse-view cryo-EM Reconstruction. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2026), 11567–11571.

== Abstract ==

Resolving conformational heterogeneity in cryo-electron microscopy
(cryo-EM) remains challenging, especially for rare
states. Standard reconstruction methods, reliant on abundant
similar particles, bias results toward dominant conformations.
To address this, we present an end-to-end pipeline
that separates orientation estimation from 3D reconstruction.
Our approach starts with self-supervised Masked Projection
Modelling (MPM), which pretrains an encoder to capture
geometric relationships across projections without known
orientations. This encoder drives a supervised Probabilistic
Orientation Estimation (POE) framework for initial orientation
inference. At testing stage on real data, a high-resolution
3D volume is estimated while orientations are further refined
within a Maximum-Likelihood Expectation-Maximization
(ML-EM) algorithm that utilizes a continuous Implicit Neural
Representation (INR), requiring no pretraining data. On
real cryo-EM data, our method achieves high-resolution reconstruction
from a severely reduced number of particle projections,
outperforming traditional methods in low-particlecount
scenarios.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/11463226/

== Related software ==

== Related methods ==

== Comments ==

2026Chen MPM - Revision history