scholarly journals Generative multi-adversarial network for striking the right balance in abdominal image segmentation

2020 ◽  
Vol 15 (11) ◽  
pp. 1847-1858
Author(s):  
Mina Rezaei ◽  
Janne J. Näppi ◽  
Christoph Lippert ◽  
Christoph Meinel ◽  
Hiroyuki Yoshida
Keyword(s):  
Medical Images ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Training Data ◽  
Class Imbalance Problem ◽  
Abdominal Image ◽  
Single Generator ◽  
Adversarial Network ◽  
Imbalance Problem ◽  
Segmentation Image

Abstract Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

2019 ◽  
Vol 33 (12) ◽  
pp. 1959037 ◽  
Author(s):  
Shaojian Qiu ◽  
Lu Lu ◽  
Siyu Jiang ◽  
Yang Guo
Keyword(s):  
Ensemble Learning ◽  
Class Imbalance ◽  
Training Data ◽  
Defect Prediction ◽  
Class Imbalance Problem ◽  
Learning Methods ◽  
Imbalance Problem ◽  
Intelligent Software ◽  
Under Sampling ◽  
Cross Project

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.

Pulsar candidate classification using generative adversary networks

2019 ◽  
Vol 490 (4) ◽  
pp. 5424-5439 ◽  
Author(s):  
Ping Guo ◽  
Fuqing Duan ◽  
Pei Wang ◽  
Yao Yao ◽  
Qian Yin ◽  
...  
Keyword(s):  
Class Imbalance ◽  
Feature Learning ◽  
Computer Experiments ◽  
Support Vector ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Imbalance Problem

ABSTRACT Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments, the volume and rate of data acquisition have grown exponentially. This development necessitates a focus on artificial intelligence (AI) technologies that can mine large astronomical data sets. Automatic pulsar candidate identification (APCI) can be considered as a task determining potential candidates for further investigation and eliminating the noise of radio-frequency interference and other non-pulsar signals. As reported in the existing literature, AI techniques, especially convolutional neural network (CNN)-based techniques, have been adopted for APCI. However, it is challenging to enhance the performance of CNN-based pulsar identification because only an extremely limited number of real pulsar samples exist, which results in a crucial class imbalance problem. To address these problems, we propose a framework that combines a deep convolution generative adversarial network (DCGAN) with a support vector machine (SVM). The DCGAN is used as a sample generation and feature learning model, and the SVM is adopted as the classifier for predicting the label of a candidate at the inference stage. The proposed framework is a novel technique, which not only can solve the class imbalance problem but also can learn the discriminative feature representations of pulsar candidates instead of computing hand-crafted features in the pre-processing steps. The proposed method can enhance the accuracy of the APCI, and the computer experiments performed on two pulsar data sets verified the effectiveness and efficiency of the proposed method.

scholarly journals Seg2pix: Few Shot Training Line Art Colorization with Segmented Image Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1464
Author(s):  
Chang Wook Seo ◽  
Yongduek Seo
Keyword(s):  
Image Data ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Segmented Image ◽  
Image Translation ◽  
Segmentation Image

There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate high quality colorized images of a particular character with only a few training data. Seg2pix is designed to reproduce a segmented image, which becomes the suggestion data for line art colorization. The segmented image is automatically generated through a generative network with a line art image and a segmentation ground truth. In the next step, this generative network creates a colorized image from the line art and segmented image, which is generated from the former step of the generative network. To summarize, only one line art image is required for testing the generative model, and an original colorized image and segmented image are additionally required as the ground truth for training the model. These generations of the segmented image and colorized image proceed by an end-to-end method sharing the same loss functions. By using this method, we produce better qualitative results for automatic colorization of a particular character’s line art. This improvement can also be measured by quantitative results with Learned Perceptual Image Patch Similarity (LPIPS) comparison. We believe this may help artists exercise their creative expertise mainly in the area where computerization is not yet capable.

scholarly journals Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3183 ◽  
Author(s):  
Zia Khan ◽  
Norashikin Yahya ◽  
Khaled Alsaih ◽  
Syed Saad Azhar Ali ◽  
Fabrice Meriaudeau
Keyword(s):  
Prostate Cancer ◽  
Data Augmentation ◽  
Prostate Gland ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Lesion Detection ◽  
Dice Similarity Coefficient ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mri Image

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8 % . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

COST-SENSITIVE NEURAL NETWORK CLASSIFIERS FOR POSTCODE RECOGNITION

2012 ◽  
Vol 26 (05) ◽  
pp. 1263001 ◽  
Author(s):  
SHUJING LU ◽  
LI LIU ◽  
YUE LU ◽  
PATRICK S. P. WANG
Keyword(s):  
Neural Network ◽  
Class Imbalance ◽  
Training Data ◽  
Training Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
The Neural Network ◽  
Sampling Cost ◽  
Recognition Systems ◽  
Neural Network Classifiers

Most traditional postcode recognition systems implicitly assumed that the distribution of the 10 numerals (0–9) is balanced. However it is far from a reasonable setting because the distribution of 0–9 in postcodes of a country or a city is generally imbalanced. Some numerals appear in more postcodes, while some others do not. In this paper, we study cost-sensitive neural network classifiers to address the class imbalance problem in postcode recognition. Four methods, namely: cost-sampling, cost-convergence, rate-adapting and threshold-moving are considered in training neural networks. Cost-sampling adjusts the distribution of the training data such that the costs of classes are conveyed explicitly by the appearances of their instances. Cost-convergence and rate-adapting are carried out in training phase by modifying the architecture of training algorithms of the neural network. Threshold-moving tries to increase the probability estimations of expensive classes to avoid the samples with higher costs to be misclassified. 10,702 postcode images are experimented using five cost matrices based on the distribution of numerals in postcodes. The results suggest that cost-sensitive learning is indeed effective on class imbalanced postcode analysis and recognition. It also reveals that cost-sampling on a proper cost matrix outperforms others in this application.

SET EXPANSION OF CONTEXTUAL SEMANTIC RELATIONS: AN ALTERNATIVE TO FULL CORPUS ANNOTATION FOR SUPERVISED CLASSIFICATION

2012 ◽  
Vol 06 (01) ◽  
pp. 93-109
Author(s):  
ANDRÉ KENJI HORIE ◽  
MITSURU ISHIZUKA
Keyword(s):  
Supervised Classification ◽  
Class Imbalance ◽  
Low Frequency ◽  
Expansion Method ◽  
Semantic Relations ◽  
Training Data ◽  
Corpus Annotation ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Incremental Improvement

Recent approaches for classification of semantic relations are based on supervised learning using large training datasets. Due to the high cost of annotating such data and to the class imbalance problem, alternatives for minimizing the effort of full corpus annotation are required. In set expansion, one of such alternatives, given a small initial training set, new relevant instances are acquired from a large corpus. However, when dealing with contextual semantic relations, which are relations that are highly dependent on the context within the sentence, set expansion is not trivial, since instances are not directly queryable and filtering requires classification under a very restricted number of training instances. This work thus proposes a bootstrapped set expansion method for contextual semantic relations. It performs a best effort extraction using the Web, and a two-stage filtering of candidate instances, the first based on syntactic patterns and the second using a feature distance-based classifier designed for the low frequency setting. The relevance of the output is measured experimentally by using the expanded set as the training data of the supervised classification task, observing an incremental improvement in performance after each bootstrapping iteration when compared to values using the unexpanded training data.

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