4 papers
Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy
Mohammad Soltaninezhad, Elena Corbetta, Francisco Paez Larios +4
Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images while reducing phototoxicity and instrumentation demands. How…
Global-to-local image quality assessment in optical microscopy via fast and robust deep learning predictions
Elena Corbetta, Thomas Bocklitz
Optical microscopy is one of the most widely used techniques in research studies for life sciences and biomedicine. These applications require reliable experimental pipelines to ex…
Lightweight CycleGAN Models for Cross-Modality Image Transformation and Experimental Quality Assessment in Fluorescence Microscopy
Mohammad Soltaninezhad, Yashar Rouzbahani, Jhonatan Contreras +4
Lightweight deep learning models offer substantial reductions in computational cost and environmental impact, making them crucial for scientific applications. We present a lightwei…
GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
Jhonatan Contreras, Thomas Bocklitz
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make…