The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

Deep Learning (DL) methods are powerful analytical tools for microscopy and can
outperform conventional image processing pipelines. Despite the enthusiasm and …

Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Reconstructing medical images from partial measurements is an important inverse problem
in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions …

Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …

Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …