multi scale
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2022 ◽  
Vol 50 ◽  
pp. 101785
Peijie Lin ◽  
Zhuang Qian ◽  
Xiaoyang Lu ◽  
Yaohai Lin ◽  
Yunfeng Lai ◽  

2022 ◽  
Vol 377 ◽  
pp. 131965
Xinfa Cai ◽  
Meijuan Liang ◽  
Fei Ma ◽  
Zhaowei Zhang ◽  
Xiaoqian Tang ◽  

2022 ◽  
Vol 73 ◽  
pp. 103476
Pak Kin Wong ◽  
Liang Yao ◽  
Tao Yan ◽  
I. Cheong Choi ◽  
Hon Ho Yu ◽  

2022 ◽  
Vol 41 (1) ◽  
pp. 1-21
Chems-Eddine Himeur ◽  
Thibault Lejemble ◽  
Thomas Pellegrini ◽  
Mathias Paulin ◽  
Loic Barthe ◽  

In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation, and classification. In this article, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM) , provide a well-suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time, and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train, and classifies millions of points in seconds.

2022 ◽  
Vol 3 (1) ◽  
pp. 1-19
Feng Lu ◽  
Wei Li ◽  
Song Lin ◽  
Chengwangli Peng ◽  
Zhiyong Wang ◽  

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

2022 ◽  
Vol 74 ◽  
pp. 103486
Huafeng Wang ◽  
Chonggang Lu ◽  
Qi Zhang ◽  
Zhimin Hu ◽  
Xiaodong Yuan ◽  

2022 ◽  
Vol 90 ◽  
pp. 104489
Jialiang Gao ◽  
Peng Peng ◽  
Feng Lu ◽  
Christophe Claramunt

2022 ◽  
Vol 124 ◽  
pp. 107255
Hongwei Wang ◽  
Yan Wang ◽  
Rui Wang ◽  
Xingli Liu ◽  
Yanyan Zhang ◽  

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