AIMC Topic: Image Processing, Computer-Assisted

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FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

IEEE transactions on medical imaging
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based o...

Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning.

IEEE transactions on medical imaging
Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets wi...

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

Medical physics
BACKGROUND: Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external trac...

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.

Physics in medicine and biology
. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.. CBCT, the same-day repeat CT, and the planning CT (pCT) o...

MD-SGT: Multi-dilation spherical graph transformer for unsupervised medical image registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deformable medical image registration is an essential preprocess step for several clinical applications. Even though the existing convolutional neural network and transformer based methods achieved the promising results, the limited long-range spatia...

Numerical and Clinical Evaluation of the Robustness of Open-source Networks for Parallel MR Imaging Reconstruction.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datase...

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Investigative radiology
OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power...

Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

Journal of dentistry
OBJECTIVES: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

AEAU-Net: an unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration.

Medical & biological engineering & computing
Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features...

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image.

Chemistry & biodiversity
The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based...