Koen Dercksen,Arjen P de Vries,Bram van Ginneken
Koen Dercksen
This study presents a direct prediction approach for estimating the prevalence of radiological findings from text, eliminating the need for computationally intensive similarity-based comparisons. The proposed method, named PRESTIGE, trades ...
Rehabilitation movement simulation via joint angle-based generative AI [0.03%]
基于关节角度的生成式AI在康复运动模拟中的应用
Gabriele Santangelo,Chiara Alessi,Giovanna Nicora et al.
Gabriele Santangelo et al.
In recent years, generative models have shown remarkable capabilities in synthesizing realistic human motion, with applications ranging from animation to virtual reality. However, their potential in clinical and rehabilitation settings rema...
The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews [0.03%]
人工智能在医疗保健实践中的应用：系统评价的映射回顾
Adam Andersen,Ruiping Huang,Edward Jiusi Liu
Adam Andersen
Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in rece...
Prior-guided multi-expert consensus fusion for multi-center thyroid nodule classification [0.03%]
基于先验引导的多中心甲状腺结节分类多专家共识融合方法
Guangju Li,Zhaoxing An,Qinghua Huang et al.
Guangju Li et al.
Thyroid nodule classification in ultrasound imaging is challenged by entangled visual patterns and distribution shifts across multi-center data due to variations in devices, protocols, and gain settings. We propose a Prior-Guided Multi-Expe...
ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification [0.03%]
ORBIT：通过双原型对比学习的肿瘤发生表示学习（用于癌症驱动基因识别）
Sang-Pil Cho,Young-Rae Cho
Sang-Pil Cho
Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propo...
GLF-Net: A quasi-periodic prior-guided waveform segmentation network for ECG delineation [0.03%]
基于心电信号切分的准周期先验引导波形分割网络(GLF-Net)
Zhenqin Chen,Yiwei Lin,Yuying Bao et al.
Zhenqin Chen et al.
Electrocardiogram (ECG) signals capture cardiac electrical activity and serve as a fundamental tool for diagnosing cardiovascular diseases. A standard ECG waveform is composed of P waves, QRS complexes, and T waves, each reflecting differen...
Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review [0.03%]
基于人工智慧的实时脑电图癫痫发作预测：系统性综述
Zikang Song,Kim Arrowsmith,Dion Henare et al.
Zikang Song et al.
Background: Epilepsy affects approximately 50 million people worldwide. While AI-driven seizure prediction shows technical promise, the transition from experimental prototypes to clinical tools is hindered by a significan...
R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform [0.03%]
基于经验模式分解和小波变换的心电图R峰检测与数据压缩方案
Xuwen Gui,Siqi Zhao,Jiacheng Zhang et al.
Xuwen Gui et al.
Background and objective: As a crucial foundation for diagnosing cardiovascular conditions, electrocardiogram (ECG) signals play a pivotal role in clinical practice. Given the exponential growth of medical data and the im...
CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection [0.03%]
基于三通道EEG的深度学习模型的跨受试者抑郁症检测.CastNet:
Shuo Zhang,Bohao Zhang,Jiaming Cai et al.
Shuo Zhang et al.
Depression is a serious mental health condition affecting millions worldwide. In recent years, deep learning models achieved remarkable performance in EEG-based depression diagnosis. This study proposes CastNet, a depression detection model...
State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions [0.03%]
结直肠癌检测的TinyML方法最新进展、挑战及未来方向
Showkat Ahmad Bhat,Ming-Che Chen,Nen-Fu Huang
Showkat Ahmad Bhat
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with diagnostic disparities, particularly pronounced in resource-constrained and decentralized healthcare settings. Recent advances in TinyML machine lea...