Deep learning is fueling advances and breakthroughs in a dizzying array of data-intensive scientific fields. This talk will highlight recent and ongoing work of our lab that leverages deep learning to push boundaries of biomedical imaging.

A long-standing challenge in the life sciences is to visualize biological cells at high resolution and with high throughput. Single molecule localization microscopy (SMLM) is among the most powerful and widely used super-resolution imaging methods, but is typically very slow. I will present ANNA-PALM, a computational technique based on deep learning that can reconstruct high resolution views from strongly under-sampled SMLM data, enabling considerable speed-ups without compromising spatial resolution. I will also highlight Shareloc, an online platform to facilitate the sharing and reanalysis of SMLM data, and show data on this platform can be used to increase the robustness of ANNA-PALM reconstructions. Potentially, preliminary applications to live cell super-resolution will also be shown.

Time permitting, I may also present additional projects, in which we use deep learning for medical imaging diagnostics or for characterizing antibiotic drugs.