6 accepted papers
Cryo-electron tomography (cryo-ET) enables in-situ visualization of cellular ultrastructure, but reconstructions are severely degraded by extremely low SNR and missing-wedge artifacts due to dose limits and restricted tilt angles. Existing learning-based approaches are further constrained by inaccur
Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationsh
Retinal angiography provides critical vascular information, yet optical coherence tomography angiography (OCTA) acquisition remains slower and less accessible than conventional structural OCT. A key open question is: how angiographic signals can be recovered? To address the issues of artifacts and s
Spatial transcriptomics provides unprecedented opportunities to analyze gene patterns while preserving spatial tissue architecture. However, traditional deep learning methods for spatial transcriptomics analysis face significant challenges in multi-modal data integration, spatial dependency modeling
Scanning transmission electron microscopy (STEM) is a powerful technique in high-resolution atomic imaging of materials. Decreasing scanning time and reducing electron beam exposure with an acceptable signal-to-noise results are two popular research aspects when applying STEM to beam-sensitive mater…
Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the S…