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SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems.
Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health.
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93. 0% and 80. 7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections respectively.
Learning with Known Operators reduces Maximum Training Error Bounds
We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
