Alexandr Dibrov

TL;DR: This work shows how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy by bypassing the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.

TL;DR: This work shows how deep learning enables biological observations beyond the physical limitations of microscopes, and illustrates how microscopy images can be restored even if 60-fold fewer photons are used during acquisition.

TL;DR: A flexible and reusable platform for GPU-acceleration in Fiji that complements core ImageJ operations with reprogrammed counterparts that take advantage of the open computer language (OpenCL) framework to execute on GPUs.

TL;DR: SEG-GRAD-CAM as discussed by the authors is a gradient-based method for interpreting semantic segmentation, applied locally to produce heatmaps showing the relevance of individual pixels for image segmentation.

TL;DR: This work proposes SEG-GRAD-CAM, a gradient-based method for interpreting semantic segmentation, an extension of the widely-used Grad-C AM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentsation.