Abstract:
With the development of convolutional neural networks (CNNs), CNN-based methods for medical image analysis have achieved more accurate performance than conventional machi...Show MoreMetadata
Abstract:
With the development of convolutional neural networks (CNNs), CNN-based methods for medical image analysis have achieved more accurate performance than conventional machine learning methods using hand-crafted features. Although these methods utilize a large number of training images and realize high performance, lack of the training images often occurs in medical image analysis due to several reasons. This paper presents a novel image generation method to construct a dataset for gastritis detection from gastric X-ray images. The proposed method effectively utilizes two kinds of training images (gastritis and non-gastritis images) to generate images of each domain by introducing label conditioning into a generative model. Experimental results using real-world gastric X-ray images show the effectiveness of the proposed method.
Date of Conference: 26-29 May 2019
Date Added to IEEE Xplore: 01 May 2019
Print ISBN:978-1-7281-0397-6
Print ISSN: 2158-1525
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- IEEE Keywords
- Index Terms
- Generative Adversarial Networks ,
- Image Generation ,
- Gastritis ,
- Gastric ,
- Convolutional Neural Network ,
- Performance Accuracy ,
- Number Of Images ,
- Training Images ,
- Handcrafted Features ,
- Detection Dataset ,
- Medical Image Analysis ,
- Label Condition ,
- Conventional Machine Learning ,
- Kinds Of Images ,
- Lack Of Images ,
- Conventional Machine Learning Methods ,
- Support Vector Machine ,
- Input Image ,
- Class Labels ,
- Support Vector Machine Classifier ,
- Patch Extraction ,
- Discriminator Network ,
- Amount Of Images ,
- Network In Order ,
- Computer-aided Diagnosis ,
- Computer-aided Diagnosis System
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Generative Adversarial Networks ,
- Image Generation ,
- Gastritis ,
- Gastric ,
- Convolutional Neural Network ,
- Performance Accuracy ,
- Number Of Images ,
- Training Images ,
- Handcrafted Features ,
- Detection Dataset ,
- Medical Image Analysis ,
- Label Condition ,
- Conventional Machine Learning ,
- Kinds Of Images ,
- Lack Of Images ,
- Conventional Machine Learning Methods ,
- Support Vector Machine ,
- Input Image ,
- Class Labels ,
- Support Vector Machine Classifier ,
- Patch Extraction ,
- Discriminator Network ,
- Amount Of Images ,
- Network In Order ,
- Computer-aided Diagnosis ,
- Computer-aided Diagnosis System
- Author Keywords