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Found 3 papers, 2 papers with code
Probabilistic volumetric speckle suppression in OCT using deep learning
We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data.
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset.
Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality Losses
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention.
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