Jiantao Qu,Dongjin Huang,Yongsheng Shi et al.
Jiantao Qu et al.
Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracti...
Deformation registration based on reconstruction of brain MRI images with pathologies [0.03%]
基于病理性脑MRI图像重建的形变配准方法研究
Li Lian,Qing Chang
Li Lian
Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor...
Closed loop automated drug infusion regulation based on optimal 2-DOF TID control approach for the mean arterial blood pressure [0.03%]
基于最优两自由度TID控制策略的平均动脉压闭环自动化药量调节方法
Oguzhan Karahan,Hasan Karci
Oguzhan Karahan
This work aims to design an optimal controller for regulating mean arterial blood pressure (MAP) during the cardiac cycle in surgical and post-surgical conditions to enhance automated drug infusion. MAP controllers must address uncertaintie...
A hardware-efficient on-implant spike compression processor based on VQ-DAE for brain-implantable microsystems [0.03%]
一种基于VQ-DAE的硬件有效神经信号压缩处理器用于脑植入微系统
Nazanin Ahmadi-Dastgerdi,Hossein Hosseini-Nejad,Hamid Alinejad-Rokny
Nazanin Ahmadi-Dastgerdi
High-density implantable neural recording microsystems deal with a huge amount of data. Since the wireless transmission of the raw recorded data leads to excessive bandwidth requirements, spike compression approaches have become vital to su...
A monocular thoracoscopic 3D scene reconstruction framework based on NeRF [0.03%]
一种基于NeRF的单目胸腔镜三维场景重建框架
Juntao Han,Ziming Zhang,Wenjun Tan et al.
Juntao Han et al.
With the increasing use of image-based 3D reconstruction in medical procedures, accurate scene reconstruction plays a crucial role in surgical navigation and assisted treatment. However, the monotonous colors, limited image features, and ob...
Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review [0.03%]
辅助需要的康复训练策略中上肢康复的患者测评方法：综述
Erkan Ödemiş,Cabbar Veysel Baysal,Mustafa İnci
Erkan Ödemiş
This paper aims to comprehensively review patient performance assessment (PPA) methods used in assist-as-needed (AAN) robotic therapy for upper extremity rehabilitation. AAN strategies adjust robotic assistance according to the patient's pe...
A novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images [0.03%]
一种基于视网膜图像环境和局部特征线索的眼底病病变新型深度学习框架
Sultan Daud Khan,Saleh Basalamah,Ahmed Lbath
Sultan Daud Khan
Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends ...
Transformer-based fusion model for mild depression recognition with EEG and pupil area signals [0.03%]
基于变压器的轻度抑郁识别融合模型及其脑电与瞳孔面积信号研究
Jing Zhu,Yuanlong Li,Changlin Yang et al.
Jing Zhu et al.
Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, ey...
Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution [0.03%]
不平衡标签分布下用于半监督乳腺癌诊断的类敏感多层注意学习
Renjun Wen,Yufei Ma,Changdong Liu et al.
Renjun Wen et al.
Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden o...
Breast cancer image classification based on H&E staining using a causal attention graph neural network model [0.03%]
基于H&E染色的乳腺癌图像分类的因果注意力图神经网络模型
Xiaoya Chang,Zhongrong Zhang,Jianguo Sun et al.
Xiaoya Chang et al.
Breast cancer image classification remains a challenging task due to the high-resolution nature of pathological images and their complex feature distributions. Graph neural networks (GNNs) offer promising capabilities to capture local struc...