A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

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CRU-Net: A Deep Learning Network for Semantic Segmentation of Pathological Tissue Slices

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) ◽

10.1109/aiid51893.2021.9456469 ◽

2021 ◽

Author(s):

Yang Li

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Tissue Slices ◽

Learning Network ◽

Deep Learning Network

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A Deep Learning Network for Point Cloud of Medicine Structure

2018 9th International Conference on Information Technology in Medicine and Education (ITME) ◽

10.1109/itme.2018.00157 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jia Guo ◽

Xuanxia Yao ◽

Mengyu Shen ◽

Jiafei Wang ◽

Wanyou Liao

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Learning Network ◽

Deep Learning Network

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Orientation-Encoding CNN for Point Cloud Classification and Segmentation

Machine Learning and Knowledge Extraction ◽

10.3390/make3030031 ◽

2021 ◽

Vol 3 (3) ◽

pp. 601-614

Author(s):

Hongbin Lin ◽

Wu Zheng ◽

Xiuping Peng

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Feature Learning ◽

Point Clouds ◽

Point Sets ◽

Learning Network ◽

Rule Structure ◽

Visual Tasks ◽

Deep Learning Network ◽

Point Cloud Classification

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

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A point-based deep learning network for semantic segmentation of MLS point clouds

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.03.001 ◽

2021 ◽

Vol 175 ◽

pp. 199-214

Author(s):

Xu Han ◽

Zhen Dong ◽

Bisheng Yang

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Point Clouds ◽

Learning Network ◽

Deep Learning Network

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Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Diagnostics ◽

10.3390/diagnostics12010066 ◽

2021 ◽

Vol 12 (1) ◽

pp. 66

Author(s):

Yung-Hsien Hsieh ◽

Fang-Rong Hsu ◽

Seng-Tong Dai ◽

Hsin-Ya Huang ◽

Dar-Ren Chen ◽

...

Keyword(s):

Deep Learning ◽

Breast Imaging ◽

Area Under The Curve ◽

Semantic Segmentation ◽

Breast Ultrasound ◽

Data System ◽

Ultrasound Images ◽

Convolutional Network ◽

Learning Network ◽

Deep Learning Network

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

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