Pressure eye: In-bed contact pressure estimation via contact-less imaging [0.03%]
无接触成像的在床压力眼：接触压力估计
Shuangjun Liu,Sarah Ostadabbas
Shuangjun Liu
Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map. ...
DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images [0.03%]
基于具有监督高斯混合模型的双分支病变感知网络的大肠内镜图像分类
Kai-Ni Wang,Shuaishuai Zhuang,Qi-Yong Ran et al.
Kai-Ni Wang et al.
Colorectal cancer is one of the malignant tumors with the highest mortality due to the lack of obvious early symptoms. It is usually in the advanced stage when it is discovered. Thus the automatic and accurate classification of early colon ...
Balamurali Murugesan,Bingyuan Liu,Adrian Galdran et al.
Balamurali Murugesan et al.
Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting in over-confident predictions. The standard practices of min...
DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging [0.03%]
基于较少取向的磁敏感张量成像中的张量重建方法研究
Zhenghan Fang,Kuo-Wei Lai,Peter van Zijl et al.
Zhenghan Fang et al.
Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for bot...
Dynamic weighted hypergraph convolutional network for brain functional connectome analysis [0.03%]
基于动态加权超图卷积网络的大脑功能连接分析
Junqi Wang,Hailong Li,Gang Qu et al.
Junqi Wang et al.
The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph ...
Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning [0.03%]
基于层次深度多重实例学习的膀胱癌组织病理图像基因突变预测
Rui Yan,Yijun Shen,Xueyuan Zhang et al.
Rui Yan et al.
Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically significant gene mutations can be predicted only th...
Distribution based MIL pooling filters: Experiments on a lymph node metastases dataset [0.03%]
基于分布的MIL聚集过滤器：在淋巴结转移数据集上的实验
Mustafa Umit Oner,Jared Marc Song Kye-Jet,Hwee Kuan Lee et al.
Mustafa Umit Oner et al.
Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success in digital histopathology thanks to its ability to handle gigapixel slides and work with ...
Low-field magnetic resonance image enhancement via stochastic image quality transfer [0.03%]
低场磁共振图像增强的随机图像质量传输方法
Hongxiang Lin,Matteo Figini,Felice D&#x;Arco et al.
Hongxiang Lin et al.
Low-field (
Unpaired, unsupervised domain adaptation assumes your domains are already similar [0.03%]
无配对、无监督领域适应算法假设领域本身已经很相似了
Gijs van Tulder,Marleen de Bruijne
Gijs van Tulder
Unsupervised domain adaptation is a popular method in medical image analysis, but it can be tricky to make it work: without labels to link the domains, domains must be matched using feature distributions. If there is no additional informati...
Self-supervised anomaly detection, staging and segmentation for retinal images [0.03%]
自监督视网膜图像异常检测、分类和分割
Yiyue Li,Qicheng Lao,Qingbo Kang et al.
Yiyue Li et al.
Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical dat...