Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Whole slide pathological images have shown significant potential for patient prognostication. Graph representation learning provides a robust framework for in-depth analysis of whole-slide images to construct predictive models. In this study, we intr...
OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while preserving image quality in high-resolution prostate diffusion-weighted imaging (DWI) with readout-...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
In the field of real-time stereo matching, a concise and informative cost volume is crucial for achieving high efficiency and accuracy. To this end, in this paper, we propose the Tri-Fusion Volume (TFV) to effectively fuse both texture details and si...
PURPOSE: To develop a multiparametric breast MRI radiomics and deep learning-based multimodal model for predicting preoperative Ki-67 expression status in breast cancer, with the potential to advance individualized treatment and precision medicine fo...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Virtual try-on technology seeks to seamlessly integrate an image of a specified garment onto the target person, generating a synthesized image that realistically depicts the person wearing the clothing. Existing methods based on generative adversaria...
PURPOSE: The purpose of this study is to perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR).
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
A key challenge in Nearshore Infrared Video Super-resolution (NIVSR) is the limited high-frequency foreground information. The most common approach is to fuse frames in order to learn cross-temporal information. However, existing methods struggle to ...
PURPOSE: To combine ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging with a self-attention convolutional neural network (SAT-Net) for accelerated mapping of macromolecular fraction (MMF) in cortical bone.
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
IEEE transactions on bio-medical engineering
Sep 1, 2025
Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.