Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods [0.03%]
Qinqin Yang,Firoozeh Shomal-Zadeh,Ali Gholipour
Qinqin Yang
Magnetic resonance imaging (MRI) has greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imaging sites often exhibit substantial heterogeneity...
Predicting neoadjuvant therapy response in breast cancer from preoperative biopsy via spatial-semantic-differential learning and interpretable clinicopathological-guided fusion [0.03%]
Wen-Tai Hou,Zi-Fei Pu,Ze-Yan Xu et al.
Wen-Tai Hou et al.
Predicting pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer remains challenging due to high tumor heterogeneity and disparities across data modalities. This study introduces a multimodal learning framework ...
Ultrasound Localization Microscopy Learned from power doppler by uncertainty frequency density estimation and semantic consistency awareness [0.03%]
Qinghua Lin,Xuan Ren,Boqian Zhou et al.
Qinghua Lin et al.
Ultrasound Localization Microscopy (ULM) achieves micron-level vascular visualization beyond the resolution of conventional ultrasound imaging by tracking microbubble positions. However, ULM relies on high-frame-count ultrasound images, whi...
Minhui Yu,Mengqi Wu,Ling Yue et al.
Minhui Yu et al.
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies ha...
GCN combined with snake convolution for enhanced topological perception in thrombotic hepatic portal vein segmentation [0.03%]
Lijuan Ma,Weiguang Wang,Xingshun Qi et al.
Lijuan Ma et al.
The hemodynamic status of the portal vein plays a crucial role in the identification, treatment, and prognostic prediction of complications associated with liver cirrhosis. Accurate segmentation of the portal vein is essential for quantitat...
Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data [0.03%]
Huidong Xie,Weijie Gan,Reimund Bayerlein et al.
Huidong Xie et al.
Reducing scan times, radiation dose, and enhancing image quality, especially for lower-performance scanners, are critical in low-count/low-dose PET imaging. Deep learning (DL) techniques have been investigated for PET image denoising. Howev...
Paula Andrea Pérez-Toro,Tomás Arias-Vergara,Fangxu Xing et al.
Paula Andrea Pérez-Toro et al.
Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treatment and rehabilitation strategies. Toward this goal, we int...
DDS-UDA: Dual-domain synergy for unsupervised domain adaptation in joint segmentation of optic disc and optic cup [0.03%]
Yusong Xiao,Yuxuan Wu,Li Xiao et al.
Yusong Xiao et al.
Convolutional neural networks (CNNs) have achieved exciting performance in joint segmentation of optic disc and optic cup on single-institution datasets. However, their clinical translation is hindered by two major challenges: limited avail...
Jinlin Yang,Xintao Pang,Chuan Lin et al.
Jinlin Yang et al.
In histopathology, cell detection and segmentation of Hematoxylin and Eosin (H&E) stained tissue images are essential clinical tasks. Existing methods decompose cell segmentation into three tasks: nucleus shape capture, overlapping nucleus ...
SEQUAL: Self-refining and effective querying active learning with pseudo label divergence score for carotid intima-media segmentation in ultrasound [0.03%]
Yucheng Tang,Yipeng Hu,Jing Li et al.
Yucheng Tang et al.
Deep learning has achieved remarkable performance in carotid intima-media (CIM) segmentation from ultrasound images, but its clinical applicability remains limited due to data scarcity, annotation variability, and low image quality. While a...