Research On Thorax Disease Classification Methods Based On Deep Learning Approaches

With the development of the medical industry and the increasing requirement for smart medical care,CAD(computer-aided diagnosis)based on artificial intelligence has received extensive attention.Medical image analysis can assist in disease diagnosis,treatment evaluation,and early warning of epidemics.Thorax disease classification aims to identify diseases based on medical images of the chest.Currently,the most widely used chest medical image is the chest X-ray(CXR).Accurate and rapid CAD based on CXR is significant for clinical applications.Thorax disease classification using deep neural networks is a challenging task.First,lesions are subtle and strongly correlated with specific organs,but existing methods do not consider in-depth the organrelated detail visual cues.Second,the lesions are scattered and correlated with different diseases.However,the existing approaches do not fully explore the contextual features.Last,different CXR datasets often have very large data shifts as well as label shifts,which may severely hinder the scalability of supervised thorax disease classification models.On the other hand,it is not trivial to directly adopt the existing unsupervised domain adaptation(UDA)methods.in the thorax disease classification task since the label shift problem will deteriorate the distribution alignment.In addition,the thorax disease classification CAD system is also very important for clinical application.To address these challenges and problems,the main research contents and innovations of this dissertation are as follows:First,this dissertation proposes a method for thorax disease classification based on multi-granularity representation learning,which is PMGAN(Part-Aware Mask-Guided Attention Network).A multi-branch network and attention modules are designed to force the network to learn complementary global and local features explicitly in an attentive manner.The multi-branch network architecture is guided by the organ mask to learn both global and local features.The mask-guided attention mechanism further searches for informative regions and visual cues within the allorgan or single-organ regions which are precisely localized by the mask.A multi-task independent learning scheme maximizes the learning of complementary global and local feature representations by simultaneously optimizing multiple losses on the same disease label.At last,PMGAN is the end-to-end trainable deep network and according to the validation experiments on the large dataset ChestX-ray 14,compared with a variety of state-of-the-art methods,PMGAN achieves thorax disease classification performance of 86.51%average AUC。Second,this dissertation proposes a method for thorax disease classification based on contextual representation learning,which is TCCNNT(Thorax Disease Classification Net Based on Joined CNN and Transformer).The method combines the advantages of CNN and Transformer to improve the classification performance.A Transformer branch network is designed to capture more contextual visual clues by shifted windows self-attention mechanism while the CNN branch is responsible for global features extraction by convolution filtering.Through joined loss training and feature fusion,the model performs thoracic disease classification.Experimental results on ChestX-ray 14 show that TC-CNNT achieves superior classification performance(AUC 86.53%).TC-CNNT outperforms PMGAN in 7 thorax disease classifications,and the combination of TC-CNNT and PGMAN can achieve the AUC performance of 86.81%.The two supervised methods can work complementarily to improve the thorax disease classification performance.Third,this dissertation proposes a method for thorax disease classification based on invariant representation learning,which is UDATC(Unsupervised Domain Adaptation-Based Thorax Disease Classification).The method aims to learn domain-invariant and discriminative feature representations from CXR data for UDA-based cross-domain thoracic disease classification.The method includes a newdesigned end-to-end trainable deep network model to learn robust and discriminative feature representations from CXR images,a UDA framework for jointly learning invariant feature representations,and a comprehensive feature learning method,which can simultaneously regularize feature representations through domain-invariant constraints,instance-invariant constraints,and perturbation-invariant constraints.To our knowledge,this is the first domain-adaptive thoracic disease classification work that simultaneously learns invariant and discriminative features.The method is validated on commonly used large-scale CXR datasets(ChestX-ray14 and SYSU).Compared with models directly crossdomain applied,on the task of SYSU→ChestX-ray14,the method exceeds by 3.15%of average AUC,and on the task of ChestX-ray14→SYSU,the method exceeds by 2.53%of average AUC.Forth,A thorax disease classification inference CAD system has been developed.An optimization strategy is proposed to decrease the complexity of the inference model.Different optimization approaches have been verified through experiments considering both accuracy and efficiency.The inference time is accelerated by 23.64 times on the NVIDIA GPU platform.A thorax disease classification inference system based on Brower/Server architecture is designed to facilitate model deployment in different scenarios.The human-computer interaction graphical interface is developed,and the important visual clues in the inference process are visualized to provide a more convenient and reasonable basis for the disease diagnosis.In summary,the research of this dissertation focuses on the deep learning-based thorax disease classification method,and the proposed methods effectively improve the performance of CXR thorax disease classification through validation.