Sharpened graph ensemble for semi-supervised learning

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data.

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“…The following function (Eq. 5) first introduced by [31] satisfies this assumption, and its solution is shown in Eq. 6.…”

Section: Data Weighing and Confident Data Labellingmentioning

confidence: 99%

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data

Sun

Tseng

Zhang

et al. 2017

Computerized Medical Imaging and Graphics

228

110

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“…As our algorithm is based on the so-called "cluster assumption" algorithm which is summarized by [29]: two points are likely to have the same class label if there is a path connecting them that passes through regions of high density only. The following quadratic objective function satisfies this assumption, and it was first introduced by [30]:…”

Section: Stage Three: Dividing Co-training Data Labelingmentioning

confidence: 99%

Computerized breast cancer analysis system using three stage semi-supervised learning method

Sun

Tseng

Zhang

et al. 2016

Computer Methods and Programs in Biomedicine

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“…Three criteria are used in this study. The first is accuracy, which evaluates how the model output matches the ground truth label [ 22 ]. Since the output of h is a pair of labels, that is, h ( x i ) = ( y i , z i ), the simple zero-one loss is not suitable in this case.…”

Section: Deep Ensemble Learningmentioning

confidence: 99%

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD‐10 Labels

Zhang

Huang

Zhong

et al. 2015

The Scientific World Journal

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Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

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Sharpened graph ensemble for semi-supervised learning

Cited by 6 publications

References 23 publications

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data

Computerized breast cancer analysis system using three stage semi-supervised learning method

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD‐10 Labels

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