Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction
Deep learning approaches for tomographic image reconstruction have become very
effective and have been demonstrated to be competitive in the field. Comparing these …
effective and have been demonstrated to be competitive in the field. Comparing these …
On the applications of neural ordinary differential equations in medical image analysis
H Niu, Y Zhou, X Yan, J Wu, Y Shen, Z Yi… - Artificial Intelligence …, 2024 - Springer
Medical image analysis tasks are characterized by high-noise, volumetric, and multi-
modality, posing challenges for the model that attempts to learn robust features from the …
modality, posing challenges for the model that attempts to learn robust features from the …
FleXCT: a flexible X-ray CT scanner with 10 degrees of freedom
BD Samber, J Renders, T Elberfeld, Y Maris… - Optics …, 2021 - opg.optica.org
Laboratory based X-ray micro-CT is a non-destructive testing method that enables three
dimensional visualization and analysis of the internal and external morphology of samples …
dimensional visualization and analysis of the internal and external morphology of samples …
2DeteCT-A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
Recent research in computational imaging largely focuses on developing machine learning
(ML) techniques for image reconstruction, which requires large-scale training datasets …
(ML) techniques for image reconstruction, which requires large-scale training datasets …
Multi-scale learned iterative reconstruction
Model-based learned iterative reconstruction methods have recently been shown to
outperform classical reconstruction algorithms. Applicability of these methods to large scale …
outperform classical reconstruction algorithms. Applicability of these methods to large scale …
The lodopab-ct dataset: A benchmark dataset for low-dose ct reconstruction methods
Deep Learning approaches for solving Inverse Problems in imaging have become very
effective and are demonstrated to be quite competitive in the field. Comparing these …
effective and are demonstrated to be quite competitive in the field. Comparing these …
Dual-domain attention-guided convolutional neural network for low-dose cone-beam computed tomography reconstruction
Excessive ionizing radiation in cone-beam computed tomography (CBCT) causes damage
to patients, whereas a low radiation dose degrades the imaging quality. To improve the …
to patients, whereas a low radiation dose degrades the imaging quality. To improve the …
Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems
Denoising diffusion models have emerged as the go-to framework for solving inverse
problems in imaging. A critical concern regarding these models is their performance on out …
problems in imaging. A critical concern regarding these models is their performance on out …
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data
Reconstruction of CT images from a limited set of projections through an object is important
in several applications ranging from medical imaging to industrial settings. As the number of …
in several applications ranging from medical imaging to industrial settings. As the number of …