V2-Former: Towards volumetric framework for instance-level segmentation and prediction of fetal ventriculomegaly in anisotropic MRI [0.03%]
V2-Former：朝向各向异性MRI下胎儿脑室扩大病灶级分割和预测的体积框架
Zhao Zhang,Lei Zhang,Wei Huang et al.
Zhao Zhang et al.
Accurate identification and assessment of fetal ventriculomegaly (VM) is crucial for prenatal care. However, conventional diagnosis relies on manual 2D slice-based measurements, which may overlook 3D morphological cues. Existing deep learni...
Fan Feng,Ting Long,Abdallah I Hasaballa et al.
Fan Feng et al.
Epicardial adipose tissue (EAT) is increasingly recognized as an important contributor to cardiovascular disease (CVD), but its spatial distribution across the heart remains insufficiently characterized due to limitations in existing segmen...
Establishing the robustness metric as a scalable proxy for clinical relevance in medical AI explainability [0.03%]
在医学人工智能可解释性中建立稳健性指标作为临床相关性的可扩展替代指标
Daehyun Cho,Sung-Hye You,Bo Kyu Kim et al.
Daehyun Cho et al.
Despite high performance of deep learning in medical imaging applications, the critical lack of validated computational metrics for explainable AI (XAI) impedes clinical integration. To address this gap, our study introduces a multi-level v...
SegMotion-Net: Segmentation-guided motion analysis for early myocardial infarction detection from echocardiography video [0.03%]
基于分割的导联运动分析的早期心肌梗死检测方法
Weitao Cai,Hao Ren,Fengshi Jing et al.
Weitao Cai et al.
Myocardial infarction (MI) remains a major clinical challenge, and early detection is essential to prevent irreversible myocardial damage. However, echocardiography-based MI detection relies heavily on physicians' subjective interpretation,...
Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks [0.03%]
基于交叉注意图神经网络的 schizophrenia 多模态MRI分类增强
Jingjing Gao,Jie Wu,Maomin Qian et al.
Jingjing Gao et al.
We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and DTI. Built on a multi-site dataset of 1191 samples from seven scanning sites, the model e...
Reconstruction-informed and multidomain deep learning for generalizable CT-free attenuation correction in SPECT myocardial perfusion imaging [0.03%]
基于重建和多域深度学习的通用型无CT衰减校正心脏SPECT显像方法
Ghasem Hajianfar,Yazdan Salimi,Mehdi Amini et al.
Ghasem Hajianfar et al.
Purpose: Deep learning (DL) has shown promise in enabling attenuation correction (AC) for SPECT myocardial perfusion imaging (MPI) without relying on anatomical information or CT-derived attenuation maps (ATMs). We introd...
Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography [0.03%]
基于物理的数据为中心的深度学习框架去除光学相干断层扫描中的饱和伪影
Jonas Nienhaus,Thomas Schlegl,Florian Kapeller et al.
Jonas Nienhaus et al.
In Fourier-domain optical coherence tomography (FD-OCT) imaging, saturation of the data acquisition chain leads to loss of information by clipping the spectral interferograms. When reconstructed, affected depth profiles are degraded by brig...
Collaborative instance-level and bag-level multiple instance learning with label disambiguation for whole slide image analysis [0.03%]
一种协作的实例级和包级多示例学习方法及其标签消解在全片扫描图像分析中的应用研究
Yu Zhao,Jun Wang,Chao Wang et al.
Yu Zhao et al.
Artificial intelligence (AI)-driven histopathological image analysis has shown significant advantages for disease diagnosis, prognosis, and treatment planning, and it is receiving growing attention in modern healthcare. Due to the gigapixel...
MoMBS: Mixed-order sampling improves training on heterogeneous-quality data for universal lesion detection [0.03%]
基于异质质量数据的混秩采样泛化病灶检测方法研究
Han Li,Jingsong Liu,Peter Schüffler et al.
Han Li et al.
Universal lesion detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. However, images in ULD tasks often exhibit substantial variations in quality, including issues such as limited clarity and inaccura...
Prompt-guided foundation model tuning for pathology image classification [0.03%]
基于提示的病理图像分类基础模型微调方法
Yi Lin,Zhengjie Zhu,Kwang-Ting Cheng et al.
Yi Lin et al.
Foundation models have become pivotal in advancing computational pathology, particularly for whole slide image (WSI) classification. However, prevailing methodologies often rely on frozen, pre-trained models for feature extraction, overlook...