Multiple-instance learning with global and local features for thyroid ultrasound image classification

2014 ◽  
Author(s):  
Jianrui Ding ◽  
H. D. Cheng ◽  
Jianhua Huang ◽  
Yingtao Zhang
Keyword(s):  
Image Classification ◽  
Ultrasound Image ◽  
Local Features ◽  
Multiple Instance Learning ◽  
Thyroid Ultrasound ◽  
Global And Local

scholarly journals Thyroid ultrasound image classification using a convolutional neural network

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Yi-Cheng Zhu ◽  
Peng-Fei Jin ◽  
Jie Bao ◽  
Quan Jiang ◽  
Ximing Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Ultrasound Image ◽  
Thyroid Ultrasound

scholarly journals Breast Ultrasound Image Classification Based on Multiple-Instance Learning

2012 ◽  
Vol 25 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Jianrui Ding ◽  
H. D. Cheng ◽  
Jianhua Huang ◽  
Jiafeng Liu ◽  
Yingtao Zhang
Keyword(s):  
Image Classification ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Multiple Instance Learning

Combining Local Features and Multi-instance Learning for Ultrasound Image Classification

2014 ◽  
Vol 39 (6) ◽  
pp. 861-867 ◽  
Author(s):  
Jian-Rui DING ◽  
Jian-Hua HUANG ◽  
Jia-Feng LIU ◽  
Ying-Tao ZHANG
Keyword(s):  
Image Classification ◽  
Ultrasound Image ◽  
Local Features

scholarly journals Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Bing ◽  
Wei Wang
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Ultrasound Image ◽  
Classification Problem ◽  
Relevance Vector Machine ◽  
Concentric Circle ◽  
Breast Ultrasound ◽  
Learning Problem ◽  
Novel Method ◽  
Global And Local

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

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