Neural Network Approaches For Mammographic Image Classification

Breast cancer is one of the most common cancers among women in most countries.Studies have shown that early precision diagnosis and timely treatment of breast cancer can save lives.Mammography is one of the most widely used methods for early breast cancer screening in the world,and also the only medical imaging method proved to significantly reduce the mortality rate of breast cancer.Mammography for breast cancer screening pro-duces several mammograms.Based on them,radiologists diagnose tumors to be benign or malignant,and the results are often highly related to the radiologists' experience.With the development of computer technology,computer-aided diagnosis systems have been used to assist doctors in diagnosing diseases.These systems,based on mammographic image,can improve efficiency and accuracy of breast cancer screening,greatly reduce the workload of doctors,and reduce the deviation of results caused by individual factors.The key part of computer-aided diagnosis system for breast cancer screening is the classification model.Mammographic image classification is also one of the research hotspots and difficulties in medical image analysis.As a kind of computational model that simulates the information processing process of biological neural networks,the neural network method has a strong feature extraction ability.The neural network method for the classification of mammographic images has importan-t theoretical value and broad application prospects.However,there are some deficiencies in existing neural network methods in mammographic image analysis.For example,the existing neural network methods are highly data-dependent but obtaining and labeling of mammographic data is difficult.Besides,the specific research of mammographic image clas-sification methods still needs improvements.In view of the characteristics of mammographic data,this thesis focuses on the research of neural network methods for mammographic image classification from two aspects:reducing data dependence and obtaining effective feature expression.In terms of reducing the data dependence of the neural network method,we s-tudy multi-instance data classification to reduce the dependence of the classification method on the label of each image and then propose a feature sensitive neural network model.Aim-ing at reducing the effect of dataset size on the performance of neural network method,we study a transfer learning method for mammographic image classification.In terms of obtaining the effective representation for classification mammographic images,we study a method to optimize the inter-class distance and inter-class distance of different categories in the classification task to obtain higher separability representations,so as to improve the classification accuracy.For the classification of breast masses,we adopted the strategy of localization first and classification after,that is,to remove the features of irrelevant regions by the localization method to improve the classification accuracy of breast masses.The main content and contributions of this dissertation are listed as follows.·A feature sensitive feature fusion method is proposed to study the classification of multi-instance mammograms.During mammography examination,several images from multiple perspectives are tak-en for each patient at one time,which are then referred by a radiologist for diagnoses.Among these pictures,if one is malignant,then the sample of this patient is malignant.For mammographic image dataset,most of the existing methods are based on a single image.This kind of method needs each image to have a precision label,which greatly increases the workload of labeling data.Moreover,the information of multi-view images is not used.To overcome the difficulty of multi-instance classification,a dataset of clinical mammographic images was first collected.Furthermore,to solve the problem of multi-instance classifica-tion,a feature sensitive based feature fusion method is proposed.The method first extracts the features of each instance image of one patient,then designs a trainable filter,which is used to evaluate the features of each instance image.If there is a malignant feature,this instance will have a large weight,otherwise this instance will have a small weight.Finally,according to these weights,multiple instance features of each patient are fused to obtain the final features.Experiments on our collected clinical dataset and some public mammography datasets demonstrate that the proposed method is superior to other similar breast cancer screening methods.Moreover,the research based on clinical data is more significant.·An adversarial domain adaptation based transfer learning method is proposed to study the transfer learning in mammography classification.The training of the neural network method cannot be achieved without the support of big data.One big difficulty is concerned with obtaining medical data.When the scale of the dataset is small,the effect of current methods may decrease dramatically.The transfer learning can transfer the knowledge learned from other datasets to the target dataset,which can relieve the performance impact caused by small dataset to some extent.In the recent literature,the commonly used transfer learning method in mammographic image analysis is to directly transfer the knowledge of pre-training in natural images without considering the differences between natural image features and medical image features,such as color space,object shape,etc.Therefore,this thesis proposes a breast cancer screening method based on domain adaptation and transfer learning.The proposed method is divided into two stages.In the first stage,unsupervised domain adaptive learning is carried out on both the public dataset and target dataset using an adversarial domain adaptation network.In the second stage,the knowledge from the unsupervised domain adaptation stage is transferred to the target dataset,and optimization is performed on the target dataset.In the first stage,the proposed method uses unsupervised domain adaptation,which aims at learning proper features so that transferring it to the target dataset and reducing the difference between the source and target dataset.And many experiments that compare the proposed method with the current state-of-the-art methods is carried out,the experimental results show that our method has achieved better or equivalent performance.·A multiple classifiers constrain based method is propoed to study the optimizing of inter-class distance and intra-class distance in the classification problem.Breast cancer screening based on mammograms is essentially the classification of benign and malignant mammographic images.To improve the classification results,feature extrac-tion models need to extract the strong discriminative features.In this thesis,a method to optimize the decision boundary of two classifiers is proposed.By constrains the decision boundary of two classifiers,the feature extraction network part can extract the feature with a larger intra-class distance and smaller inner-class distance.Using the two classifiers,at the same time,they can detect the samples with inconsistent classification results.This kind of sample is distributed near the decision boundary,and the classifier is difficult to learn to separate.By weighting the loss function of this kind of sample during the training process,the feature extraction network pays more attention to the features of this kind of samples.Experiments on multiple public datasets demonstrate the effectiveness of the proposed method.·A reinforcement learning and multi-task learning object localization method is pro-posed.Based on localization and then classification strategy,by using the localization results to classify benign and malignant masses,which improves the classification results of benign and malignant masses.For the classification of masses in mammograms,a commonly used way is to separate out the masses by using a localization or segmentation method and then classify these masses.The object localization methods can be divided into two categories,the bottom-up and the top-down strategy.In the top-down strategy,the localization can be regarded as a decision make process.The reinforcement learning method is good at learning the process of object search and decision-making.The existing top-down strategy method based on reinforcement learning needs to store the historical states and use it for training the neural network,and the stored states itself are an imbalanced sampling,which makes the decision-making process difficult to learn.To better learn the decision-making process of localization objects,we propose a method that using a multi-task learning method,so that the neural network can make use of the advantages of multi-task learning in structure.At the same time,when dividing into multi-tasks,the original imbalanced sampling problem has been broken,and the minor sampling class is separated into one independent task,which alleviates the performance bottleneck brought by imbalanced sampling.Finally,the method in this paper was verified on natural image dataset and mammographic image dataset.The experimental results showed that the proposed method in the thesis could localize the objects more accurately.The results of benign and malignant tumor classification were improved by further apply the proposed method to the classification of mammograms.