AIMC Topic: Image Processing, Computer-Assisted

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Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

Journal of biomedical informatics
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and m...

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Genomics, proteomics & bioinformatics
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor mic...

The PHU-NET: A robust phase unwrapping method for MRI based on deep learning.

Magnetic resonance in medicine
PURPOSE: This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods.

B-Map: a fuzzy-based model to detect foreign objects in a brain.

Medical & biological engineering & computing
To cope up with the medical complications, scientists and physicians rely more on digitized historical evidence. It helps them to identify the disease and to develop new drugs and strategies. The authors have designed a model called B-Map. It can det...

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction.

NeuroImage
Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with para...

Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention.

Sensors (Basel, Switzerland)
With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. T...

Contrast-enhanced to noncontrast CT transformation via an adjacency content-transfer-based deep subtraction residual neural network.

Physics in medicine and biology
To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional impor...

The overview of the deep learning integrated into the medical imaging of liver: a review.

Hepatology international
Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. Thi...

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

NeuroImage
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of b...

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment.

Sensors (Basel, Switzerland)
Gamification is known to enhance users' participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various ra...