With the advent of higher resolution imaging modalities, such as electron microscopy (Cryo-EM, FIB-SEM) and fluorescence superresolution microscopy (SRM), scientists are able to discern subcellular structures at the molecular level leading to discoveries in basic and translational sciences as well as applications in drug discovery and precision medicine. Visualizing cellular, sub-cellular, and protein structures have been recently recognized with Nobel Prizes in Chemistry, for super-resolution fluorescence microscopy in 2014 and for cryo-electron microscopy in 2017. SRM imaging achieves nanometer resolution while cryo-EM allows imaging of structures in their native, frozen, hydrated state by resolving structural details of 1.5 Angstrom. These imaging modalities as well as many others rely on performant computational techniques to reconstruct high resolution images in 2D and 3D for visualization and further quantitative analysis. Advances in machine learning, particularly in deep learning, has a great potential to contribute to high resolution reconstruction process, particularly by improving particle detection and classification steps of reconstruction.
This second workshop aims to bring the researchers from computational and imaging fields together to have a wider focus on the computational approaches that learn parameters from image data while maintaining an emphasis on the leading edge machine learning methods such as deep learning for all computational tasks: segmentation, classification, construction, and analysis in high resolution imaging modalities (cryo-electron microscopy, FIB-SEM tomography and fluorescence superresolution microscopy).
Original contributions in applications of deep learning and other machine learning methods in high resolution microscopy including but not limited to noise reduction, detection, segmentation, classification, and reconstruction of 2D and 3D models, as well as new approaches in 3D reconstruction of single molecules are welcome. Contributions regarding other related modalities and automated analysis methods such as single molecule tracking, multiphoton imaging, and combining EM with STORM/PALM will also be considered for inclusion in the workshop program.
Please submit a full-length original and unpublished research contribution (up to 6 pages in IEEE 2-column format) through the online submission system (you can download the format instruction here (http://www.ieee.org/conferences_events/conferences/publishing/templates.html). Electronic submissions in PDF are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance. All papers will be published in conference proceedings and indexed by IEEE Xplore. Workshop organizers may select a small number of workshop papers to be extended and published in a journal.
Online Submission: https://wi-lab.com/cyberchair/2019/bibm19/index.php
Workshop Program Committee Members: