CA-OCL and CHAN: A novel diagnostic framework for rheumatoid arthritis integrating contradiction-aware orthogonal contrastive learning with confidence-guided hierarchical attention [0.03%]
基于矛盾感知正交对比学习与置信度引导分层注意机制的新型诊断框架用于类风湿性关节炎
Zhao Huang,QingMei Zeng,NanNan Gai
Zhao Huang
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive destruction of synovial joints, for which precise early diagnosis is critical to effective clinical management. Although current computer-aided...
Weakly-supervised ultrasound image segmentation with elliptical shape prior constraint [0.03%]
基于椭圆先验约束的弱监督超声图像分割方法
Changyan Wang,Yehua Cai,Ruyi Yang et al.
Changyan Wang et al.
Accurate pixel-level segmentation of ultrasound (US) images is vital for computer-aided disease screening, diagnosis, and treatment response evaluation. The weakly supervised methods have the potential to reduce the time-consuming and labor...
Deformable phrase level attention: A flexible approach for improving AI based medical coding [0.03%]
可变形的短语级注意力机制：一种灵活地提升基于人工智能的医疗计费编码准确率的方法
Christoph Metzner,Shang Gao,Drahomira Herrmannova et al.
Christoph Metzner et al.
Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel atten...
Enhancing transformer-based architectures with geometric deep learning for colonoscopic polyp size classification using transfer learning [0.03%]
基于几何深度学习的变压器架构改进，用于结肠镜多腺体尺寸分类的迁移学习
Adrian Krenzer,Stefan Heil,Frank Puppe
Adrian Krenzer
Accurate estimation of polyp size during colonoscopy is critical for risk assessment and surveillance planning in colorectal cancer prevention. However, current methods often rely on subjective visual judgment, leading to inconsistencies an...
Detecting depression through speech and text from casual talks with fully automated virtual humans [0.03%]
通过与全自动虚拟人类的日常对话中的语音和文字检测抑郁症
Lucía Gómez-Zaragozá,Alberto Altozano,Jose Llanes-Jurado et al.
Lucía Gómez-Zaragozá et al.
Depression is a significant global health issue with increasing prevalence. Current diagnostic methods rely on subjective observations and questionnaires, often resulting in underestimation of the condition and insufficient treatment. This ...
Electrohysterography in modern obstetrics: Advances in signal processing, machine learning, and clinical applications [0.03%]
现代妇产科中的电子宫肌ogram技术：信号处理、机器学习及临床应用方面的进展
Katerina Barnova,Radek Martinek,Jitka Horakova et al.
Katerina Barnova et al.
Electrohysterography (EHG) represents a promising computational approach for non-invasive monitoring of uterine activity during pregnancy and labor. This review summarizes the advancements in signal processing techniques and machine learnin...
Frequency-based boundary-guided attention network for domain generalizable polyp segmentation from colonoscopy images [0.03%]
基于频率的边界引导注意力网络在结肠镜图像中进行领域通用息肉分割
Ju-Hyeon Nam,Sang-Chul Lee
Ju-Hyeon Nam
Colonoscopy is the most effective method for detecting colorectal polyps and preventing colorectal cancer. The accurate segmentation of polyps in colonoscopy images is crucial for diagnosis and surgery, which remains a challenge in various ...
Shu-Cheng Chen,Yiliang Chen,Wing-Fai Yeung et al.
Shu-Cheng Chen et al.
Objectives: The systematic review aimed to comprehensively summarize evidence from the existing literature on using deep learning (DL) techniques in the practice of acupuncture. ...
Hierarchical classification for differential diagnosis of fever of unknown origin: A multi-task learning approach with self-adaptive representation sharing [0.03%]
用于未知病因发热鉴别诊断的层次分类方法：一种自适应表征共享的多任务学习方法
Zhixiao Wang,Yu Tian,Jian Liu et al.
Zhixiao Wang et al.
Leveraging label dependencies as prior knowledge during both training and testing has proven valuable across diverse domains such as image annotation and text categorization. In our previous research, we successfully reframed the clinical c...
Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries [0.03%]
多注释一致性和预测一致性网络：利用模糊边界的医学图像半监督分割改进
Shuai Wang,Tengjin Weng,Jingyi Wang et al.
Shuai Wang et al.
Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised setti...