Joint Denoising of Cryo-EM Projection Images using Polar Transformers

Deep neural networks~(DNNs) have proven powerful for denoising, but they are ultimately of limited use in high-noise settings, such as for cryogenic electron microscopy~(cryo-EM) projection images. In this setting, however, datasets contain a large number of projections of the same molecule, each taken from a different viewing direction. This redundancy of information is useful in traditional denoising techniques known as class averaging methods, where images are clustered, aligned, and then averaged to reduce the noise level. We present a neural network architecture based on transformers that extends these class averaging methods by simultaneously clustering, aligning, and denoising cryo-EM images. Results on synthetic data show accurate denoising performance using this architecture, reducing the relative mean squared error (MSE) single-image DNNs by $45\%$ at a signal-to-noise (SNR) of $0.03$.

Ablation Path Saliency

Various types of saliency methods have been proposed for explaining black-box classification. In image applications, this means highlighting the part of the image that is most relevant for the current decision. We observe that several of these methods can be seen as edge cases of a single, more general procedure based on finding a particular ablation path through the classifier's domain. This gives additional geometric insight to the existing methods. We also demonstrate that this ablation path method can be used as a technique in its own right, the higher computational cost being traded against additional information given by the path.