Research On The Key Issues Of Small Sample Classification And Class Imbalance Classification In Medical Image Aided Diagnosis

Medical image-aided diagnosis is one of the key issues in the field of medical image analysis and plays an important role in modern clinical disease diagnosis.In recent years,deep learning technology has been widely used in various medical image-aided diagnosis tasks due to its excellent feature expression ability and powerful data fitting performance.However,as a datadriven modeling method,deep learning relies heavily on a large number of samples to train the model,and the imbalance in the number of samples between different categories in the dataset will also seriously affect the performance of deep learning models.Due to existing problems such as few sample acquisition channels,high labeling costs,strong data privacy,and large differences in the incidence of different disease types,medical imaging datasets often suffer from small number of samples and an imbalance in the number of samples between categories,which will bring great challenges to the construction of high-performance deep learning classification models.In order to cope with the above challenges,this thesis focuses on the key issues of small sample classification and class-imbalance classification in medical image-aided diagnosis,starting with key technologies such as multi-task learning,attention mechanism,loss function optimization as well as semi-supervised learning,and proposes a variety of small sample classification methods and class-imbalance classification methods,which effectively reduce the requirements of deep learning modeis on massive data and the balance of sample numbers between different categories.The main research contents and innovations of this thesis are summarized as follows:(1)A small sample classification method based on multi-task learning and Siamese networks is proposed.Aiming at the problem that the deep learning model is difficult to extract the distinguishing features of samples between categories in the small-sample medical imaging aided diagnosis task,based on multi-task learning,by designing an auxiliary classification task that can capture the similarity of samples in same category and the differences of samples between different categories,and then combining it with a common classification task,a deep model with Siamese network(DS-Net)is constructed,which can effectively improve the classification performance of deep learning models on small sample datasets.The DS-Net model consists of an auxiliary supervised network(ASN)based on the Siamese network and a general classification network(CN).In the construction process of DS-Net,in order to enhance the feature extraction ability of the model,a dilated residual block(DRB)and DRB network are proposed,which will then be used to construct the backbone networks of ASN and CN,so that the model can extract features of the same scale with different receptive fields.During model training.DS-Net uses the idea of paired learning and takes paired data as input.ASN extracts inter-class differences and intra-class similarity features by judging whether the paired samples belong to the same category;the classification network performs target classification tasks,and uses the discriminative features extracted by ASN to improve its own classification performance.In the test phase,DS-Net does not need to use paired samples,and can obtain prediction results only using a single input data.The experimental results on the task of necrotic area assessment in osteosarcoma based on pathological image show that DS-Net can obtain an average accuracy of 95.10%,achieving the best diagnostic result so far.(2)A small sample classification method is proposed based on a densely connected attention mechanism.Aiming at the problem that the deep learning model does not pay enough attention to the key region of the sample in the small-sample medical image-aided diagnosis task,from the perspective of the spatial attention mechanism,a densely connected attention network(DenseANet)is proposed,which is verified on the intelligent diagnosis task of corona virus disease 2019(COVID-19)based on chest CT images.In the construction process of DenseANet,in order to make full use of the self-attention features in the deep learning model,a densely connected attention block(DAB)for generating strong attention features is designed,and a densely connected attention sub-network(DA-SNet)is constructed by densely connecting attention features of the same scale within DAB blocks and between DAB blocks.At the same time,in order to further enhance the model’s ability to represent high-order features,an attention feature aggregation block(DA-FAB)that can densely connect attention features at different scales is designed at the end of the model to further enhance the feature expression.With the increase of the number of deep learning model layers,the spatial attention features at different scales and depths can be densely connected and gradually transmitted to the back end of the model,so that the DenseANet model can output diagnostic results based on strong attention features.Experimental results show that DenseANet can effectively locate lung lesions infected by SARS-CoV-2 virus,and can distinguish COVID-19,common pneumonia and healthy people with an average accuracy of 95.69%,which is better than existing attention models.(3)A small sample classification method is proposed based on an active attention mechanism.Aiming at the problem that the deep learning model is difficult to accurately locate the lesion region in the small-sample medical imaging aided diagnosis task,a prior knowledge-based active attention network(PKA2-Net)is designed.PKA2-Net consists of residual blocks,subject enhancement and background suppression(SEBS)blocks,and candidate template generators,where template generators are used to generate candidate templates to describe the importance of different spatial positions in a feature map.The SEBS block is the core of PKA2-Net,which is used to generate active attention features to enhance the model’s ability to localize lesion regions.From a structural point of view,the SEBS block designed based on the prior knowledge that highlighting obvious features and suppressing irrelevant features will improve the classification effect can actively generate supervision information,and then generate active attention features by calibrating current features.In the PKA2-Net model,the generation process of active attention features does not require the correction of high-level features to low-level features,which solves the problem that some inaccurate high-level features may cause excessive dispersion of attention features,which in turn leads to deviations in the lesion localization process.The proposed PKA2-Net is verified on the pneumonia diagnosis task based on chest X-ray images,and experimental results show that PKA2-Net can identify pneumonia patients with an accuracy of 97.28%and a sensitivity of 0.9846 on the public ChestXRay2017 dataset,achieving the state-of-the-art classification results.(4)A class-imbalance prediction method based on a novel Harmony loss function is proposed.Aiming at the problem that the deep learning model degrades the recognition ability of minority(classes with a small number of samples)samples in the unbalanced medical image prediction problem,according to the characteristic that the area under Precision-Recall curve(AUCPR)is sensitive to each category of sample,a statistically significant Harmony loss function(Harmony Loss)is designed.Since AUCPR is ralculated on the discrete domain to ensure that Harmony Loss is continusly differentiable and has a smoth gradient,we first approximat AUCPR on the continuous domain by using the Logistic function.Then,in order to improve the optimization speed of the model during training process,the AUCPR is further approximated by manually setting a certain number of thresholds.Through the above two approximate calculation processes,a Harmony Loss with stable gradient and high computational efficiency is constructed.During model optimization,Harmony Loss can improve the model’s ability to identify minority samples and can keep the model training curve stable by reconciling recall and precision of each category in prediction results under different thresholds.We comprehensively evaluated the effect of Harmony Loss on the two dense prediction tasks of 3D defective skull reconstruction and brain tumor segmentation,as well as the two sparse prediction tasks of diabetic retinopathy grading and diagnosis of various pathological types of pulmonary nodules.Experiments show that the proposed Harmony Loss can greatly improve the sensitivity of deep learning models to minority samples,and outperform existing loss functions in the imbalance prediction problem.(5)An unbalanced small-sample classification method is proposed based on semi-supervised adversarial learning.Aiming at the problem that the deep learning model is prone to overfitting and easy to reduce the sensitivity of minority class samples in the small sample and class imbalance medical image aided diagnosis issue,a reverse adversarial classification network(RACN)is proposed based on semi-supervised learning,which is validated in the diagnosis task of multiple pathological types(adenocarcinoma,squamous cell carcinoma,inflammatory and other benign diseases)of pulmonary nodules.The RACN model consists of a reverse generative adversarial network(RGAN)for performing unsupervised regression task and a supervised classification network(CN).In the design process of RGAN,four specific normal distributions with different means and variances are specified to represent four pathological types of pulmonary nodules.And then a special uncertainty regression task is designed by mapping pulmonary nodules to random samplings from the corresponding normal distribution to extract specific features that can help enhance the classification ability of CN.Since the goal of uncertainty regression task is a randomly generated vector satisfying the hypothetical distribution during each iteration of the model optimization,the model will introduce certain uncertainties during model training,which is beneficial to reduce the overfitting risk of deep learning models on small sample datasets.At the same time,the variance attribute of normal distribution can control the regression difficulty of corresponding hypothetical distribution,and then control the attention of RACN model to minority samples and majority(classes with a large number of samples)samples,which can solve the problem that the deep learning model is easy to reduce the recall of minority samples in unbalanced datasets.The experimental results on the self-constructed unbalanced small-sample lung thin-section CT image dataset show that the RACN model has achieved an average accuracy of 88.62%in the classification task of multiple pathological types of pulmonary nodules,and can identify malignant nodules with an accuracy of 93.21%on the public LIDC-IDRI dataset,obtaining the state-of-the-art results.