Abstract:
This paper presents a method of semi-supervised learning based on tri-training for gastritis classification using gastric X-ray images. The proposed method is constructed...Show MoreMetadata
Abstract:
This paper presents a method of semi-supervised learning based on tri-training for gastritis classification using gastric X-ray images. The proposed method is constructed based on the tri-training architecture, and the strategies of label smoothing regularization and random erasing augmentation are utilized in the method to enhance the performance. Although the task of gastritis classification is challenging, we report that the proposed semi-supervised learning method using only a small number of labeled data achieves 0.888 harmonic mean of sensitivity and specificity on test data composed of 615 patients.
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
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Semi-supervised Learning ,
- Classification Of Gastritis ,
- Classification Task ,
- Harmonic Mean ,
- Semi-supervised Methods ,
- Harmonic Mean Of Sensitivity ,
- Training Set ,
- Training Data ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Real-world Applications ,
- Final Evaluation ,
- Convolutional Neural Network Model ,
- Random Values ,
- Labeled Samples ,
- Unlabeled Data ,
- Convolutional Neural Network Training ,
- Data Augmentation Methods ,
- Original Implementation ,
- Model M2 ,
- Pseudo Labels ,
- Model M3 ,
- Model M1 ,
- Support Vector Machine Training ,
- Accurate Labels ,
- Output Logits
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Semi-supervised Learning ,
- Classification Of Gastritis ,
- Classification Task ,
- Harmonic Mean ,
- Semi-supervised Methods ,
- Harmonic Mean Of Sensitivity ,
- Training Set ,
- Training Data ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Real-world Applications ,
- Final Evaluation ,
- Convolutional Neural Network Model ,
- Random Values ,
- Labeled Samples ,
- Unlabeled Data ,
- Convolutional Neural Network Training ,
- Data Augmentation Methods ,
- Original Implementation ,
- Model M2 ,
- Pseudo Labels ,
- Model M3 ,
- Model M1 ,
- Support Vector Machine Training ,
- Accurate Labels ,
- Output Logits
- Author Keywords
