Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes [0.03%]
用于2型糖尿病患者非酒精性脂肪肝预测的双图注意力网络模型
Tianbin Chen,Yongbin Zeng,Jinlin Wang et al.
Tianbin Chen et al.
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, which can progress to more severe liver diseases such as liver fibrosis, cirrhosis ...
Ankur Gogoi & Nirmal Mazumder (Eds.). Biomedical imaging: Advances in artificial intelligence and machine learning. Singapore: Springer nature, 2024. 345 pp. €119.38 (e-book). ISBN: 978-981-97-5345-1. doi:10.1007/978-981-97-5345-1 [0.03%]
安库尔·戈吉亚和尼尔马尔·玛祖姆德（编）：《生物医学成像：人工智能与机器学习进展》. 新加坡：施普林格自然出版公司，2024年. 345页. €119.38（电子书）. ISBN: 978-981-97-5345-1. doi:10.1007/978-981-97-5345-1
Emil Salim,Heldalia Heldalia
Emil Salim
From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology [0.03%]
从幻灯片到人工智能就绪地图：作为数字病理学中人工通用智能元数据的标准多层组织地图
Gernot Fiala,Markus Plass,Robert Harb et al.
Gernot Fiala et al.
A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewabl...
IKDP: Implicit Knowledge Enhanced Disease Prediction via heterogeneous admission sequence graphs [0.03%]
基于异构住院序列图的隐式知识增强疾病预测(IKDP)
Zongbao Yang,Yuchen Lin,Yichen He et al.
Zongbao Yang et al.
Despite significant advances in deep learning for electronic health record (EHR) modeling, accurately representing complex disease relationships and admission trajectories remains challenging. Current approaches that leverage external knowl...
BRLA-DDI: A novel framework for drug-drug interaction extraction [0.03%]
基于注意力机制和依存树的药物相互作用抽取框架BRLA-DDI
Zhu Yuan,Shuailiang Zhang,Zongjin Li et al.
Zhu Yuan et al.
Drug-drug interaction (DDI) extraction is a pivotal task in biomedical information processing, focused on identifying potentially adverse drug reactions (ADRs). Despite significant progress in DDI extraction, existing models struggle with c...
Rethinking U-Net architecture in medical imaging: Advancing the efficient and interpretable UKAN-CBAM framework for colorectal polyp segmentation [0.03%]
重新思考医学影像中的U-Net架构：提出一种高效且可解释的UKAN-CBAM框架以实现结肠息肉分割
Md Faysal Ahamed,Fariya Bintay Shafi,Md Rabiul Islam et al.
Md Faysal Ahamed et al.
Prompt detection of colorectal polyps is essential for preventing colorectal cancer, a leading cause of cancer-related deaths worldwide. However, manual detection through medical imaging faces significant challenges, including high costs, r...
Mitigating data center bias in cancer classification: Transfer bias unlearning and feature size reduction via conflict-of-interest free multi-objective optimization [0.03%]
用于癌症分类的数据中心偏差缓解：通过无利益冲突的多目标优化消除迁移偏见和减小特征尺寸
Farnaz Kheiri,Shahryar Rahnamayan,Masoud Makrehchi
Farnaz Kheiri
Bias in the decision-making processes of trained deep models poses a significant threat to their reliability. Such bias can lead to overoptimistic results on observed data while compromising generalization to unseen datasets. Training data ...
Siamese evolutionary masking: Enhancing the generalization of self-supervised medical image segmentation model [0.03%]
暹罗进化掩膜：增强自监督医学图像分割模型的泛化能力
Yichen Zhi,Hongxia Bie,Jiali Wang et al.
Yichen Zhi et al.
Self-supervised learning autonomously extracts features from unlabeled data, supporting downstream segmentation tasks with limited annotations. However, variations in devices, imaging parameters, and other factors lead to differences in the...
The accuracy, validity and reliability of Theia3D markerless motion capture for studying the biomechanics of human movement: A systematic review [0.03%]
Theia3D无标记动作捕捉技术在人类运动生物力学研究中的准确性、有效性和可靠性：系统综述
Florent Varcin,Mark G Boocock
Florent Varcin
Recent advancements in computer vision recognition combined with the use of pose estimation algorithms has led to a rapid increase in the use of 3D video-based markerless (ML) motion capture to study human movement. One such prominent syste...
Integrating probabilistic trees and causal networks for clinical and epidemiological data [0.03%]
概率树和因果网络在临床和流行病学数据中的整合方法研究
Sheresh Zahoor,Pietro Liò,Gaël Dias et al.
Sheresh Zahoor et al.
Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional machine learning (ML) models excel at predicting outcomes, such as identifying high-risk pati...