Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning
Explore machine learning methods for object detection, semantic segmentation, and instance segmentation in cryo-electron micrographs. Delve into unsupervised object detection using geometric deep learning and variational autoencoders for automatic particle detection and classification in cryoEM. Discover new techniques for semantic segmentation of filaments and membranes in micrographs and tomograms, as well as a graph-based transformer for instance segmentation of point clouds. Learn how these approaches leverage geometric deep learning to build known invariants into model architectures, enhancing accuracy and data efficiency. Gain insights into the challenges of 3D instance segmentation for complex microtubule networks and the development of end-to-end tomogram analysis tools combining semantic and instance segmentation.