Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The abundance of available unlabeled data can be used in this scheme. developed data ighing module and redesigned similarity function. designed and combined local features and global features together.Our system has acceptable sensitivity even at a low false positive rate. Background and ObjectiveA large number of labeled medical image data is usually a requirement to train a ll-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. MethodsIn this study  developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data ighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features re incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy re computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who undernt routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions re extracted and used in this study, and among them 90 regions re treated as labeled while the rest re treated as unlabeled. ResultsUsing our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference beten using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. ConclusionsThis study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications.
['Wenqing Sun', 'Tzu Liang Bill Tseng', 'Jianying Zhang', 'Wei Qian']
I am writing a research paper on a idea Computerized breast cancer analysis system using three stage semi-supervised learning method
[{"from": "human", "value": "I am writing a research paper on a idea Computerized breast cancer analysis system using three stage semi-supervised learning method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The abundance of available unlabeled data can be used in this scheme. developed data ighing module and redesigned similarity function. designed and combined local features and global features together.Our system has acceptable sensitivity even at a low false positive rate. Background and ObjectiveA large number of labeled medical image data is usually a requirement to train a ll-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. MethodsIn this study  developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data ighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features re incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy re computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who undernt routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions re extracted and used in this study, and among them 90 regions re treated as labeled while the rest re treated as unlabeled. ResultsUsing our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference beten using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. ConclusionsThis study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications."}]