Navigating the pitfalls of applying machine learning in genomics
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
Deep learning for tomographic image reconstruction
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 …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
[HTML][HTML] Democratising deep learning for microscopy with ZeroCostDL4Mic
Deep Learning (DL) methods are powerful analytical tools for microscopy and can
outperform conventional image processing pipelines. Despite the enthusiasm and …
outperform conventional image processing pipelines. Despite the enthusiasm and …
Robust compressed sensing mri with deep generative priors
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 …
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
Deep learning techniques for inverse problems in imaging
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 …
wide variety of inverse problems arising in computational imaging. We explore the central …
Implicit neural representation in medical imaging: A comparative survey
A Molaei, A Aminimehr, A Tavakoli… - Proceedings of the …, 2023 - openaccess.thecvf.com
Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Solving inverse problems in medical imaging with score-based generative models
Reconstructing medical images from partial measurements is an important inverse problem
in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions …
in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions …
DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction
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 …
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
Deep equilibrium architectures for inverse problems in imaging
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 …
architectures inspired by a fixed number of iterations of an optimization method. The number …