The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
PYRO‐NN: Python reconstruction operators in neural networks
Purpose Recently, several attempts were conducted to transfer deep learning to medical
image reconstruction. An increasingly number of publications follow the concept of …
image reconstruction. An increasingly number of publications follow the concept of …
[HTML][HTML] TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …
different challenges compared to RGB images typically used in computer vision. These …
[HTML][HTML] State-of-the-art review on deep learning in medical imaging
Deep learning (DL) is affecting each and every sphere of public and private lives and
becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the …
becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the …
A systematic collection of medical image datasets for deep learning
The astounding success made by artificial intelligence in healthcare and other fields proves
that it can achieve human-like performance. However, success always comes with …
that it can achieve human-like performance. However, success always comes with …
Deep learning computed tomography
In this paper, we demonstrate that image reconstruction can be expressed in terms of neural
networks. We show that filtered back-projection can be mapped identically onto a deep …
networks. We show that filtered back-projection can be mapped identically onto a deep …
On hallucinations in tomographic image reconstruction
Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-
posed inverse problems are typically regularized using prior knowledge of the sought-after …
posed inverse problems are typically regularized using prior knowledge of the sought-after …
Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions
Computed tomography (CT) is widely used in medical diagnosis and non-destructive
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
Transfer learning for medical images analyses: A survey
The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …
science and also revitalized numerous fields where traditional machine learning methods …
Deep learning and vision transformer for medical image analysis
Artificial intelligence (AI) refers to the field of computer science theory and technology [1] that
is focused on creating intelligent machines capable of simulating human intelligence [2]. AI …
is focused on creating intelligent machines capable of simulating human intelligence [2]. AI …