Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker [0.03%]
基于回归的卷积神经网络结肠镜多发息肉检测算法研究
Ruikai Zhang,Yali Zheng,Carmen C Y Poon et al.
Ruikai Zhang et al.
A computer-aided detection (CAD) tool for locating and detecting polyps can help reduce the chance of missing polyps during colonoscopy. Nevertheless, state-of-the-art algorithms were either computationally complex or suffered from low sens...
Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis [0.03%]
基于结构稀疏正则化的多核学习在阿尔茨海默病诊断中的应用研究
Jialin Peng,Xiaofeng Zhu,Ye Wang et al.
Jialin Peng et al.
Multimodal data fusion has shown great advantages in uncovering information that could be overlooked by using single modality. In this paper, we consider the integration of high-dimensional multi-modality imaging and genetic data for Alzhei...
On Automated Source Selection for Transfer Learning in Convolutional Neural Networks [0.03%]
卷积神经网络中迁移学习的自动源选择方法研究
Muhammad Jamal Afridi,Arun Ross,Erik M Shapiro
Muhammad Jamal Afridi
Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned fea...
Robert O&#x;Brien,Hemant Ishwaran
Robert O&#x;Brien
Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q*
Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks [0.03%]
基于深度卷积网络的全视场乳腺组织病理图像癌症检测与分类
Baris Gecer,Selim Aksoy,Ezgi Mercan et al.
Baris Gecer et al.
Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for clinically more significant multi-class scenarios where intermediate categories have different risk factors and treatment strategies. We present a ...
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images [0.03%]
稀疏自动编码器在组织学图像中的无监督核检测与识别
Le Hou,Vu Nguyen,Ariel B Kanevsky et al.
Le Hou et al.
We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps tha...
A context-sensitive deep learning approach for microcalcification detection in mammograms [0.03%]
一种基于深度学习的乳腺X线片微钙化检测方法
Juan Wang,Yongyi Yang
Juan Wang
A challenging issue in computerized detection of clustered microcalcifications (MCs) is the frequent occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We develop a context-sensitive deep neural network (D...
Simultaneous segmentation and bias field estimation using local fitted images [0.03%]
基于局部拟合图像的同时分割和偏场估计方法
Lei Wang,Jianbing Zhu,Mao Sheng et al.
Lei Wang et al.
Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different l...
Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution [0.03%]
基于学习的超分辨率融合高厚度诊断磁共振图像的大脑图谱
Jinpeng Zhang,Lichi Zhang,Lei Xiang et al.
Jinpeng Zhang et al.
It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic image...
Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation [0.03%]
基于全片数字化病理图像的计算机辅助神经母细胞瘤评价：分级神经分化的类型
J Kong,O Sertel,H Shimada et al.
J Kong et al.
Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic ...