.png){kind=icon}
返回
Multi-path x-D recurrent neural networks for collaborative image classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2020.02.033.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rapid development of image acquisition and storage, multiple images per class are commonly available for computer vision tasks (e.g., face recognition, object detection, medical imaging, etc.). Recently, the recurrent neural network (RNN) has been widely integrated with convolutional neural networks (CNN) to perform image classification on ordered (sequential) data. In this paper, by permutating multiple images as multiple dummy orders, we generalize the ordered RNN+CNN design (longitudinal) to a novel unordered fashion, called Multi-path x-D Recurrent Neural Network (MxDRNN) for image classification. To the best of our knowledge, few (if any) existing studies have deployed the RNN framework to unordered intra-class images to leverage classification performance. Specifically, multiple learning paths are introduced in the MxDRNN to extract discriminative features by permutating input dummy orders. Eight datasets from five different fields (MNIST, 3D-MNIST, CIFAR, VGGFace2, and lung screening computed tomography) are included to evaluate the performance of our method. The proposed MxDRNN improves the baseline performance by a large margin across the different application fields (e.g., accuracy from 46.40% to 76.54% in VGGFace2 test pose set, AUC from 0.7418 to 0.8162 in NLST lung dataset). Additionally, empirical experiments show the MxDRNN is more robust to category-irrelevant attributes (e.g., expression, pose in face images), which may introduce difficulties for image classification and algorithm generalizability. The code is publicly available. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
RNN
Longitudinal
Unordered image
Category-irrelevant attributes
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images在整个幻灯片图像上使用弱监督深度学习的临床级计算病理学.png)
NATURE MEDICINEOAAI
NATURE MEDICINEIF50
OAAI分享收藏The expression of human intercellular adhesion molecule‐2 is refractory to inflammatory cytokines
PREAI
分享收藏Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical ImagingCLINICAL CANCER RESEARCHOAAI
CLINICAL CANCER RESEARCHIF10.2
OAAI分享收藏End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography基于低剂量胸部ct的三维深度学习端到端肺癌筛查NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional NetworksCT图像中的肺结节检测: 使用多视图卷积网络减少假阳性OAAI
OAAI分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
查看更多内容