• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    2024-05-25 14:39:40LudaChenKuangzhuBaoYingChenJingangHaoandJianfengHe
    Computers Materials&Continua 2024年4期

    Luda Chen ,Kuangzhu Bao ,Ying Chen ,Jingang Hao,? and Jianfeng He,3,?

    1Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,650504,China

    2Department of Radiology,Second Affiliated Hospital of Kunming Medical University,Kunming,650101,China

    3School of Physics and Electronic Engineering,Yuxi Normal University,Yuxi,653100,China

    ABSTRACT Pancreatic diseases,including mass-forming chronic pancreatitis(MFCP)and pancreatic ductal adenocarcinoma(PDAC),present with similar imaging features,leading to diagnostic complexities.Deep Learning(DL)methods have been shown to perform well on diagnostic tasks.Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region.However,over-reliance on prior information may ignore the background information that is helpful for diagnosis.This study verifies the diagnostic significance of the background information using a clinical dataset.Consequently,the Prior Difference Guidance Network (PDGNet) is proposed,merging decoupled lesion and background information via the Prior Normalization Fusion(PNF)strategy and the Feature Difference Guidance(FDG)module,to direct the model to concentrate on beneficial regions for diagnosis.Extensive experiments in the clinical dataset demonstrate that the proposed method achieves promising diagnosis performance:PDGNets based on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and 89.98%,marking improvements of 8.19%and 7.64%over the prior-free benchmark.Compared to lesion-focused benchmarks,the uplift is 6.14% and 6.02%.PDGNets based on advanced networks reach an ACC and AUC of 89.77%and 92.80%.The study underscores the potential of harnessing background information in medical image diagnosis,suggesting a more holistic view for future research.

    KEYWORDS Pancreatic cancer;pancreatitis;background region;prior normalization fusion;feature difference guidance

    1 Introduction

    Accurate differentiation between Mass-Forming Chronic Pancreatitis (MFCP) and Pancreatic Ductal Adenocarcinoma (PDAC) is crucial in clinical practice due to the substantial differences in treatment approaches and prognoses [1].Both subtypes have similar features in various medical imaging modalities,presenting as localized pancreatic masses[2].This similarity increases the risk of misdiagnosis[3].For instance,some studies indicate that approximately 5%to 15%of pancreatitis is diagnosed as pancreatic cancer[4].Accurate preoperative diagnosis is crucial for distinguishing MFCP from PDAC[5].

    Radiologists accurately differentiate MFCP and PDAC without invasive procedures,basing their judgments on extensive experience and comprehensive references of multimodal data in the preoperative period.It is time-consuming and makes it impossible to ensure stable diagnosis in clinical practice.The application of deep learning in medical image analysis provides a solution to improve the accuracy and efficiency of diagnosis.There are three main research directions for deep learning-based image diagnosis of pancreatic lesions:1)prior-free end-to-end diagnostic networks,2)prior-injected cascade diagnostic networks,and 3)prior-injected parallel diagnostic networks.

    Prior-free end-to-end diagnostic networks use original images as the training set for the diagnostic model,as shown in Fig.1a.For example,Ziegelmayer et al.[6]used the VGG-19[7]architecture,pretrained on ImageNet [8],to accomplish the task of feature extraction and diagnostic differentiation between autoimmune pancreatitis (AIP) and PDAC.Such studies required large-scale datasets and more complex network structures to avoid the interference of redundant information.Notably,the relatively small percentage of the pancreatic lesion region in the image presents a challenge for priorfree networks,making capturing detailed information difficult.

    Prior-injected cascade diagnostic networks use a segmentation or detection model to identify the lesion region in original images,are used as the training set for the diagnostic model,as shown in Fig.1b.For example,Si et al.[9] used a full end-to-end deep learning approach that consists of four stages: Image screening,pancreas localization,pancreas segmentation,and pancreas tumor diagnosis.Qu et al.[10] first reconstructed the pancreas region through anatomically-guided shape normalization,then used an instance-level contrast learning and balance adjustment strategy for the early diagnosis of pancreatic cancer.Li et al.[11]designed a multiple-instance-learning framework to extract fine-grained pancreatic tumor features,followed by an adaptive-metric graph neural network and causal contrastive mechanism for early diagnosis of pancreatic cancer.Chen et al.[12]designed a dual-transformation-guided comparative learning scheme based on intra-space-transformation consistency and inter-class specificity.This scheme aimed to mine additional supervisory information and extract more discriminative features to predict pancreatic cancer lymph node metastasis.

    Prior-injected parallel diagnostic networks process the segmentation or detection task in a cascade network,running parallel to the diagnostic task.For example,Zhang et al.[13] first extracted the localization information of the tumor through the augmented feature pyramid network.They then enhanced this localization information with a self-adaptive feature fusion and dependencies computation module,enabling the simultaneous performance of pancreatic cancer detection and diagnosis tasks.Xia et al.[14] used a novel deep classification model with an anatomy-guided transformer to detect resectable pancreatic masses.They classified it as PDAC,other abnormalities(nonPDAC),and normal pancreas.Zhou et al.[15] proposed a meta-information-aware dual-path transformer consisting of a Convolutional Neural Network (CNN) based segmentation path and a transformer-based classification path.This design enabled the simultaneous handling of tasks related to detecting,segmenting,and diagnosing pancreatic lesion locations.

    Prior-injected diagnostic networks align better with radiologists’diagnostic mode.Focusing the analysis on the lesion region may avoid the interference of non-pathological changes in the image or irrelevant physiological information on the model training.However,these deep learning-based approaches have some limitations: 1) the diagnostic model’s performance strongly depends on the accuracy of segmentation or detection results,and biases in these results may mislead the diagnostic model,and 2) pancreatic lesions may cause nearby organs or tissues’morphologic and physiologic alterations [16,17].For example,PDAC,when infiltrating the duodenum,typically encircles the stomach and duodenal artery,resulting in bile duct dilation and pronounced jaundice.In contrast,the MFCP may not exhibit these effects[18].The model,which relies primarily on the lesion region,may ignore contextually significant diagnostic information.Therefore,efficiently leveraging this prior information while preserving information integrity and minimizing redundancy presents a critical challenge.

    Figure 1: Existing deep learning-based diagnostic frameworks for pancreatic lesions.(a)the prior-free diagnostic network,(b) the prior-injected diagnostic network,and (c) the prior difference guidance network(ours)

    For this purpose,the study involves the collection of an authentic dataset from MFCP and PDAC patients in a clinical environment.The dataset undergoes two initial exploratory experiments to assess the influence of prior information on diagnostic models’performance.Such prior information,acquired before the deep learning model training,encompasses lesion regions in MFCP and PDAC,identified directly by radiologists through annotations based on their expertise,and background regions,which are calculated indirectly by masking these lesion areas.Preliminary experiments indicate that background regions,typically considered“noise”in deep learning,offer valuable clues essential for the diagnostic process.

    Drawing on the insights,this study introduces the Prior Difference Guidance Network(PDGNet),as shown in Fig.1c.Unlike existing models,the PDGNet utilizes decoupled lesion and background information to direct the model to concentrate on beneficial regions for diagnosis.The Prior Normalization Fusion(PNF)strategy,the component of this network,integrates the prior information of lesions and backgrounds with the original image before the data is fed into the model.The strategy enables the model to access richer contextual information than the original image.Additionally,the Feature Difference Guide (FDG) module,which employs comparative learning,is proposed.The module further utilizes the prior-augmented lesion and background information,capturing the difference between the lesion region’s and the background region’s augmented features.These differences guide the model to adjust the focus region adaptively according to the importance of the decisions,to achieve a more accurate identification and differentiation between MFCP and PDAC.The main contributions of this study are summarized as follows:

    ? The study introduces a novel diagnostic framework,the Prior Difference Guidance Network(PDGNet),which uniquely utilizes decoupled lesion and background information to improve the accuracy of differentiating between MFCP and PDAC.

    ? The study develops the Prior Normalization Fusion (PNF) strategy,an innovative approach within PDGNet that integrates the prior information of lesions and backgrounds with the original image before processing,to enrich the model’s input with a broader context.

    ? The study implements the Feature Difference Guide(FDG)module,introducing a comparative learning approach that exploits the differences between the augmented features of the lesion and background regions,to direct the model to concentrate on beneficial regions for diagnosis for decision-making adaptively.

    2 Materials and Preliminary Analysis

    The study investigates the impact of prior information on deep learning-assisted diagnosis for MFCP and PDAC tasks.Authentic datasets of MFCP and PDAC patients from clinical settings are collected.Based on these datasets,two validation experiments are designed:One to examine the influence of images without the lesion region on the diagnostic model,and the other to assess the effect of the background region on the diagnostic model.

    2.1 Dataset

    A comprehensive dataset is collected from the Second Affiliated Hospital of Kunming Medical University,including arterial-phase abdominal Magnetic Resonance Imaging(MRI)sequences of 31 MFCP patients and 62 PDAC patients.The dataset includes 3,872 slices,with 375 slices annotated to indicate lesion regions.Fig.2 illustrates the slice-image with the lesion region.

    Figure 2: Illustration of MFCP and PDAC lesions.The top row shows the MFCP lesion slice-image,and the bottom row shows the PDAC lesion slice-image.(a)shows the original image,(b)Shows the lesion region with a masked background,(c) shows the lesion region after crop and resize,and (d)shows the background region with a masked lesion

    Inclusion criteria:1) Patients with MCFP and PDAC confirmed by surgery and/or biopsy histopathology,and 2)MRI scanning within 1 month before neoadjuvant chemotherapy or surgery.

    Exclusion criteria:Lesions were poorly visualized or showed non-mass-like enhancement that was difficult to outline.

    Scanning machine:Planar and dynamic enhancement scans of the upper abdomen were performed using a Siemens Sonata 1.5 Tesla(1.5 T)MR scanner.

    Scanning sequence and parameters:Transverse,coronal,and sagittal scans were performed in VIBE sequence using gadopentetate dextran(0.2 ml/kg)during the arterial phase(25–30 s).

    The lesion region annotation criteria:Initially,an experienced radiologist utilizes 3D Slicer software(https://www.slicer.org/) to label the entire tumor as comprehensively as possible,avoiding areas of necrosis,calcification,and gases that can obscure the lesion.To ensure accuracy,the labeled tumor area is subsequently reviewed by another radiologist.

    2.2 Preliminary Experiment

    This study involves randomly selecting 300 slice-images that contain lesion regions from the dataset.This selection establishes the base training for a preliminary diagnostic model for PDAC with MFCP.The percentage of slice-images without the lesion region in the training set is incrementally increased,to train multiple diagnostic models,as shown in Fig.3a.These models undergo evaluation using the same test set,with specific experimental results presented in Table 1,and visualized as shown in Fig.4a.

    Table 1: Diagnostic performance using varying IMAGE proportions in the training set.Where n1 represents the number of images with lesion region,n2 represents those without lesion region,defined by the equation n2=300×r

    Figure 3: Schematic diagram of experimental designs:(a)Experiment 1 investigates the impact of nonlesion images on the diagnostic model.(b) Experiment 2 investigates the impact of the background region on the diagnostic model

    In another experiment,the lesion regions from 300 slice-images are extracted and utilized to create the new training set.The proportion of background region within this lesion region is progressively increased,to train several diagnostic models,as shown in Fig.3b.These models are evaluated on the same test set,with specific experimental results presented in Table 2,and visualized as shown in Fig.4b.

    Table 2: Diagnostic performance using varying REGION proportions in the training set.Where r=0%represents using the lesion region’s maximum diameter as the side length of the cropped rectangle.The proportion of background in the rectangle is increased by r times this side length

    The VGG-11 architecture serves as the foundational diagnostic network for this study.All training sessions are conducted under uniform parameter settings.The slice-images designated for the training and test sets originate from distinct patients.

    The experimental results lead to the following conclusions and insights:1)the prior information plays an important role in deep learning-assisted diagnosis,according to the experimental results in Tables 1 and 2.The model’s performance fluctuates when the proportion of lesion and background region in the training data changes,2)the model’s performance is not optimal when only lesion region images are used for training,according to the experimental results in Table 1.With the increase of nonlesion region images,the model’s performance is improved in some cases,indicating that it is beneficial to maintain a certain balance of diseased and non-diseased images for the diagnostic task of PDAC with MFCP,and 3)the model’s performance starts to decrease when the proportion of background region increases to a certain extent,which indicates that the background information holds significant value in the diagnostic task,according to the experimental results in Table 2.However,exceeding a specific percentage interferes with the model’s performance.

    Figure 4: Visualization of experimental results: (a) ACC curve of the testing set as r varies with the proportion of non-lesion images in the training set,and (b) ACC curve of the testing set as r varies with the proportion of the background region in the training set

    The insights from the comparative analysis of two sets of experiments inform subsequent model design enhancements.These improvements include:1)a data augmentation strategy that maximizes the utilization of contextual information during training,to intensify the focus on identified lesion regions,thereby augmenting the recognition of these critical regions,and 2)an attention fusion module that enables it to dynamically adjust its focus on the lesion region and the relevant portions of the contextual regions,allowing for a more accurate diagnosis of PDAC and MFCP.

    3 Methods

    The analysis leads to the proposal of a Prior Difference Guidance Network(PDGNet),with its structure illustrated in Fig.5.The Network consists of two main components:The Prior Normalization Fusion(PNF)strategy and the Feature Difference Guidance(FDG)module.

    Figure 5: The structure of the prior difference guidance network (PDGNet),with two components:The prior normalization fusion(PNF)strategy and the feature difference guidance(FDG)module

    3.1 Prior Normalization Fusion(PNF)Strategy

    The Prior Normalization Fusion (PNF) strategy for data augmentation is proposed,as shown in Fig.6.Before the data is input into the model,it tries to fuse the prior information of lesion and background with the original image,which enables the model to obtain richer contextual information than the original image when performing diagnosis.

    Specifically,the lesion region is initially selected based on the optimal background occupancy ratio (r=60%),as determined in preliminary experiments.Subsequently,the background region is extracted by masking the lesion region in the original image.The original image is then overlaid with the prior images (both lesion and background).Normalization is conducted within the prior local region,considering only non-zero regions to prevent the diluted effect of a global homogeneous background on the normalized fused image.

    Figure 6: The structure of the prior difference guidance network (PDGNet),with two components:The prior normalization fusion(PNF)strategy and the feature difference guidance(FDG)module

    Given an imageI,let the lesion region beD.μprioris the average of the gray values of all pixels in the lesion(or background)region.σprioris the standard deviation of the gray values of all pixels within the lesion(or background)region.

    whereI(i)denotes the gray value of pixeliin imageI,|D|denotes the number of pixels in the lesion region,and?is a very small value used to avoid the case where the denominator is zero.The PNF strategy is essentially a linear contrast stretching method that augments the contrast by stretching the range of pixel values of the image.This strategy augments the feature recognizability of the prior region while preserving the full contextual information of the original image.

    3.2 Feature Difference Guidance(FDG)Module

    The PDGNet introduces a Feature Difference Guidance (FDG) module to utilize the prioraugmented lesion and background information further,as shown in Fig.7.The module further utilizes the prior-augmented lesion and background information,capturing the difference between the lesion region’s and the background region’s augmented features.These differences guide the model to adjust the focus region adaptively according to the decisions’importance.

    Figure 7: The structure of the prior difference guidance network (PDGNet),with two components:The prior normalization fusion(PNF)strategy and the feature difference guidance(FDG)module

    The module combines the original image with prior-augmented lesion and background information fusion images as inputs.This integration offers richer and multi-perspective contextual information for model training.Features z1,z2,and z3are extracted from these images using distinct encoders.z2and z3represent the full image encoding of lesion-augmented and background-augmented images,respectively.The magnitude of the difference between z2and z3reflects the relative importance of specific regions in the image for diagnostic decisions,guiding the model to focus more on regions with significant differences.

    The overall framework is shown in Fig.4,defines the backbone network comprising n blocks,with an FDG module added after each block.In the first n-1 blocks,z2,z3,and weightedas inputs to the next block.In the last(nth)block,feature splicing of z2,z3and weightedand then input them to the classifier to get the diagnosis.Loss calculation is done using the cross-entropy loss function.

    4 Results

    4.1 Implementation Details

    The training and test sets are divided by case with a 9:1 ratio to prevent mutual data leakage within the same case.The training set contains arterial-phase abdominal MRI sequences of 82 patients(27 MCFP,55 PDAC),with a total of 3,432 slices(326 slices annotated with lesion areas),and the test set contains 11 cases(4 MCFP,7 PDAC),with a total of 440 slices(49 slices annotated with lesion areas).A total of 440 slices(49 with lesion areas labeled).

    For preprocessing,the image resolution is adjusted to 224 × 224,and image augmentation is applied using restricted contrast adaptive histogram equalization.In the experiments,the total number of epochs is set to 300.The learning rate is initialized at 1e-4 and dynamically adjusts using a cosine function,with a minimum value set at 1e-6 and a loop count of 50.An early-stopping mechanism is employed to prevent overfitting,terminating training if the loss value of the validation set does not decrease for 30 epochs.The batch size is 64,and the model is optimized using adaptive moment estimation(AdamW)[19]with a weight decay of 1e-3.Additionally,all experiments use the PyTorch framework on an NVIDIA GeForce RTX 4090 graphics processing unit.

    4.2 Evaluation Metrics

    This study employs a comprehensive evaluation of the diagnostic performance of the model using several metrics:Accuracy(ACC),area under the subject operating characteristic curve(AUC),sensitivity/recall (SEN/REC),specificity (SPE),precision (PREC) and F1 score (F1).These metrics are defined below:

    Accuracy (ACC):Accuracy measures the proportion of all cases (both MFCP and PDAC) that are correctly identified by the model at a specific threshold,calculated asACC=(TP+TN)/(TP+FP+TN+FN).High accuracy in differentiating MFCP from PDAC indicates the model’s overall effectiveness in distinguishing these two conditions.

    Area Under the Curve(AUC):AUC refers to the area under the Receiver Operating Characteristic(ROC) curve,a graphical representation of a model’s diagnostic ability.It measures the model’s capability to discriminate between two classes (MFCP and PDAC) across all possible threshold values.A higher AUC value implies that the model performs better in distinguishing between negative(MFCP) and positive (PDAC) cases,regardless of any specific threshold set for classifying cases as positive or negative.

    Sensitivity/Recall (SEN/REC):This metric quantifies the model’s ability to correctly identify positive cases(PDAC),calculated asSEN=REC=TP/(TP+FN).High sensitivity in diagnosing PDAC means the model can effectively identify most true PDAC cases,reducing the risk of missed diagnoses,which is vital for timely and accurate diagnosis.

    Specificity (SPE):Specificity measures the model’s ability to correctly identify negative cases(MFCP),calculated asSPE=TN/(TN+FP).In this study,high specificity indicates that when the model identifies a sample as not being PDAC (i.e.,MFCP),this judgment is likely correct.This is crucial for preventing misdiagnosis of MFCP as PDAC,which could lead to unnecessary and potentially harmful treatments.

    Precision (PREC):Precision reflects the proportion of cases identified as positive (PDAC) that are indeed PDAC,calculated asPREC=TP/(TP+FP).High precision is particularly important in diagnosing PDAC to ensure that most cases diagnosed as PDAC are indeed PDAC,minimizing false positives.

    F1 Score (F1): The F1 score is the harmonic mean of precision and recall,calculated asF1=2×(REC×PREC)/(REC+PREC).In distinguishing MFCP from PDAC,the F1 score provides a composite measure that balances recall and precision,helping to assess the model’s performance in maintaining a balance between these two metrics.

    TP,TN,FP,and FN represent the number of true-positive,true-negative,false-positive,and falsenegative samples,respectively.

    These metrics are intended to offer a holistic view of the model’s performance,covering various aspects of diagnostic accuracy.Each metric offers insights into different dimensions of the model’s effectiveness,ensuring a thorough evaluation of its capabilities in medical diagnosis.

    4.3 Effectiveness of the Prior Normalization Fusion(PNF)Strategy

    The study explores the effectiveness of the Prior Normalization Fusion(PNF)strategy by selecting ResNet-18[20]as the baseline model,and comparing various data input types as the training set.These include the original image,the cropped and resized lesion region,the background region obtained by masking the lesion region,the lesion-augmented and background-augmented images obtained by the PNF strategy,and the lesion-augmented and background-augmented images obtained by the global normalization fusion strategy(GNF).

    Furthermore,to examine the impact of attention mechanisms on the models,the study evaluates Squeeze-and-Excitation (SE) [21] without the prior information condition,Convolutional Block Attention Module (CBAM) [22],Channel Prior Convolutional Attention (CPCA) [23],and Vision Transformer (ViT) [24] based on the global spatial attention mechanism Self-Attention [25].The corresponding results are shown in Table 3.

    Table 3: Performance comparison using different data types as inputs and augmented by different attention mechanisms

    Table 3 indicates that ResNet-18,when trained with lesion-augmented and backgroundaugmented images using the PNF strategy,surpasses the performance of models trained with images augmented by the GNF strategy or models trained with original images.In particular,the lesionaugmented image obtained by the PNF strategy achieves an ACC of 84.77%,a 5.46%improvement compared to the original image,and a 3.41%improvement compared to using only the lesion region.These results validate the superiority of the PNF strategy.Without utilizing the prior information,the SE attention mechanism improves the ACC of ResNet-18 to 80.22%,while the performance of CBAM,CPCA,and ViT is lower than that of the benchmark network model.

    4.4 Effectiveness of the Feature Differential Guidance(FDG)Module

    This study utilizes the lesion-augmented and background-augmented images obtained by the PNF strategy,and the original image as the training set,with ResNet-18 serving as the baseline model,to assess the role of the Feature Difference Guidance(FDG)module.

    Additionally,the impact of various fusion strategies on diagnostic performance is examined.These strategies include:1)Slicer-Dimension Concatenate:Connect the three images in the slicer dimension before modeling,2) Channel-Dimension Concatenate: Connect the three images in the channel dimension,and 3) ResNet-18+Feature Concatenate: Extract features using different encoders for each input image type,and then connect the features after each block of the model.The results are displayed in Table 4.

    Table 4: Performance comparison using different fusion strategies

    As described in Tables 3 and 4,ResNet-18,based on FDG modules,demonstrates the best performance with an ACC of 87.5%,higher than the other strategies.In addition,the other fusion strategies also brought performance improvements,reaching an ACC of 86.13%when feature linking was performed within the model.

    4.5 Ablation Experiments

    The study conducts ablation experiments on four mainstream backbones,ResNet,ViT,Swin Transformer [26],and ConvNeXt [27],to further explore the benefits of the PNF strategy and the FDG module.The relevant results are listed in Table 5,and the ROC curves are shown in Fig.8.

    Table 5: Results of the ablation experiments for the prior normalization fusion(PNF)strategy and the feature difference guidance(FDG)module based on different backbone network

    Figure 8: Average ROC curves and AUC values of the ablation experiments based on different backbone network

    Table 5 and Fig.8 illustrate that the implementation of the PNF strategy and the FDG module significantly improves the performance of models based on the CNN architecture,specifically ResNet-18 and ConvNeXt,as well as those based on the transformer architecture,such as ViT and Swin Transformer.This evidence underscores the effectiveness,superior generalization ability,and compatibility of these strategies across various network architectures.

    4.6 Comparison with Other Methods

    The study aims to differentiate between MFCP and PDAC using arterial phase MRI scans.The PDGNet is compared to other pancreatic lesion diagnostic models,including the prior-free diagnostic network by Ziegelmayer et al.,which employed VGG-19 to distinguish between AIP and PDAC[6].Si et al.[9] used a prior-injected diagnostic network,which was first trained using the U-Net32 [28]to train a pancreas segmentation model,and then input the segmentation results into ResNet34 to distinguish between five different pancreatic lesions.The study employs manual annotation instead of the segmentation results from U-Net.

    As described in Table 6,the PDGNet based on ConvNeXt outperforms other models on all evaluation metrics for MFCP and PDAC classification tasks.This further demonstrates that the implemented strategies can effectively alleviate the problem of the difficulty of discriminative feature extraction.

    Table 6: Performance comparison with other deep learning-based pancreatic lesion diagnosis methods

    The comparison may not be fair since the studies used different datasets,but it can still provide valuable references for future research.

    5 Discussion

    The study investigates a concept frequently overlooked in existing research on deep learning for pancreatic lesion diagnosis: Background region considered as “noise”can actually provide valuable information for diagnostic models.As shown in Tables 1 and 2,the ACC and AUC of the diagnostic model reach 63.26%and 65.65%,even though the training set consists entirely of images without lesion regions.When masking the lesion region from the complete image containing the lesion region,the ACC and AUC are 67.34%and 71.67%,underlining the significance of the background region in the diagnostic modeling dataset.

    Consequently,the Prior Normalization Fusion(PNF)strategy is proposed.The strategy,which fuses prior information before data input into the model,augments the feature recognizability of the prior (lesion and background) region while preserving the complete contextual details of the original image.As shown in Table 3,without utilizing the prior information,channel attention SE can only bring relatively limited performance improvement,with ACC and AUC increasing by 0.91% and 0.48%,respectively.In contrast,introducing spatial attention leads to a decline in model performance.This could be attributed to inherent noise in the image,causing a bias in the attention mechanism without prior information.However,the GNF and PNF strategies demonstrate significant performance gains,particularly the PNF strategy,which improves the ACC and AUC of the benchmark network model by 5.46%and 4.11%,respectively.

    Otherwise,the study observes that both lesion-augmented and background-augmented images generated by the PNF strategy are able to improve the diagnostic model’s performance.To explore the potential of this prior-augmented information more deeply,a Feature Difference Guidance (FDG)module is introduced.The module combines the original image with the lesion-augmented and background-augmented images so that they jointly participate in the model training process.The superiority of this fusion strategy is further confirmed by the data in Table 5,where the FDG module demonstrates the best performance.

    Ablation experiments on convolutional neural networks such as ResNet-18,ConvNeXt,and Transformer-based ViT,Swin Transformer,show that the proposed Prior Difference Guidance Network (PDGNet) with the PNF strategy and the FDG module achieve significant improvements on all four frameworks.Especially on ConvNeXt,the ACC and AUC of the model are improved to 89.77%and 92.80%,respectively.

    In summary,the study confirms that the background region carries useful information for diagnosis,which the model should more fully utilize.The PDGNet,incorporating the PNF strategy and FDG module,significantly improves the diagnostic accuracy for MFCP and PDAC,uniquely leveraging prior information from lesion and background regions.

    Although the model achieves excellent performance,it has some limitations.For example,the clinical datasets utilized might lack diversity and size.Nevertheless,the network demonstrates robustness and effectiveness in data diversity and size constraints by accurately extracting and analyzing key discriminative features.This offers promise for application in a wider range of clinical scenarios.Secondly,a notable shortcoming of the proposed approach is the extensive training time and large model parameters.Therefore,with continued optimization and algorithmic improvements,there is an expectation of significant reductions in training time and improvements in model efficiency.Thirdly,extensive testing in real-world clinical settings is yet to be conducted for the model.However,preliminary findings and the model’s theoretical design indicate that,with further refinement and validation,it will serve as an effective tool for assisted diagnosis in clinical environments.Future research will focus on collecting more clinical data to enhance the model’s generalization ability,exploring more efficient algorithms and network architectures to optimize the training process and minimize computational resource requirements,and conducting validations in actual clinical settings to confirm its effectiveness and feasibility.The ultimate goal is to improve the accuracy and reliability of automated diagnosis,aiming to implement these models in clinical practice and offering more effective diagnostic tools for physicians and patients.

    6 Conclusions

    This study proposes a novel approach for deep learning pancreatic lesion diagnostic research,focusing on the lesion region and fully utilizing the information in the background region.The study observes that even in background regions without obvious lesions,valuable information exists that helps with diagnosis.Drawing on this insight,the Prior Difference Guidance Network (PDGNet)significantly improves the performance of MFCP and PDAC diagnostic models through the Prior Normalization Fusion(PNF)strategy and the feature difference guidance(FDG)module.

    The PNF strategy preserves the complete contextual information of the original image.It augments the feature recognizability of the prior region by fusing the prior information of the lesion and the background region.The FDG module,on the other hand,combines the original image with the augmented lesion and background fusion image so that both of them participate in the model’s training process,further improving the model’s accuracy.Ablation experiments conduct on various prominent deep learning networks,including ResNet-18,ConvNeXt,Vision Transformer(ViT),and Swin Transformer,substantiate the effectiveness of this approach.

    In conclusion,the study emphasizes the importance of contextual information in deep learning pancreatic lesion diagnosis and proposes new methods to utilize this information more fully to improve model performance.The study provides a valuable reference for future medical image diagnosis.It suggests that scholars should not only focus on salient target regions but also pay full attention to the background information that is often overlooked.

    Acknowledgement:The authors would like to express their gratitude to Prof.He and Prof.Hao for supervising of this study.

    Funding Statement:This research is supported by the National Natural Science Foundation of China (No.82160347);Yunnan Key Laboratory of Smart City in Cyberspace Security (No.202105AG070010);Project of Medical Discipline Leader of Yunnan Province(D-2018012).

    Author Contributions:The authors confirm contribution to the paper as follows:Conceptualization,L.C.and K.B.;data curation,K.B.and Y.C.;investigation,L.C.,K.B.and Y.C.;methodology,L.C.;formal analysis,L.C.;project administration,J.H.(Jianfeng He);supervision,J.H.(Jianfeng He)and J.H.(Jingang Hao);writing—original draft preparation,L.C.;writing—review and editing,L.C.,J.H.(Jianfeng He)and J.H.(Jingang Hao).All authors have read and agreed to the published version of the manuscript.

    Availability of Data and Materials:The datasets generated during this study are not publicly available due to privacy and ethical considerations;however,anonymized data can be provided by the corresponding author upon reasonable request and with the approval of the Ethics Committee.Researchers interested in accessing the data should contact the corresponding author (Prof.Hao,kmhaohan@163.com)for further information.

    Ethics Approval:The study was conducted in accordance with the Declaration of Helsinki,and approved by the Ethics Committee of the Second Affiliated Hospital of Kunming Medical University(No.2023-156).

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    久久亚洲精品不卡| 又爽又黄无遮挡网站| 一级毛片aaaaaa免费看小| 国产成人freesex在线| 91久久精品电影网| 日韩大片免费观看网站 | 夜夜爽夜夜爽视频| 夜夜看夜夜爽夜夜摸| 亚洲精品成人久久久久久| 舔av片在线| 人妻少妇偷人精品九色| 国产淫语在线视频| 天天躁夜夜躁狠狠久久av| 国产精品一及| 亚洲四区av| videos熟女内射| 亚洲色图av天堂| 国产一区二区三区av在线| 国产精品爽爽va在线观看网站| 性色avwww在线观看| 亚洲国产精品久久男人天堂| 欧美不卡视频在线免费观看| av国产免费在线观看| 深爱激情五月婷婷| 国产中年淑女户外野战色| 免费人成在线观看视频色| 精品人妻偷拍中文字幕| 在线免费观看的www视频| 国产精品久久视频播放| 观看免费一级毛片| 国产精品1区2区在线观看.| 国产一区二区在线观看日韩| 一夜夜www| a级毛色黄片| 成人午夜精彩视频在线观看| 国产高潮美女av| 亚洲精品色激情综合| 国产在视频线精品| 男女那种视频在线观看| 插逼视频在线观看| 国产精品一区二区三区四区久久| 日本一二三区视频观看| 亚洲人成网站高清观看| 久久久久久九九精品二区国产| 长腿黑丝高跟| 在线免费观看不下载黄p国产| 99热精品在线国产| 亚洲精品自拍成人| 亚洲av一区综合| 成年免费大片在线观看| 日韩国内少妇激情av| av播播在线观看一区| 亚洲最大成人av| 热99re8久久精品国产| 99久久精品一区二区三区| 国产高潮美女av| 有码 亚洲区| 欧美色视频一区免费| 久久久a久久爽久久v久久| 精品国产露脸久久av麻豆 | 亚洲欧洲日产国产| 色视频www国产| 国产成人一区二区在线| 少妇被粗大猛烈的视频| 国产黄片视频在线免费观看| 老司机福利观看| 国内精品一区二区在线观看| 亚洲成人中文字幕在线播放| 一二三四中文在线观看免费高清| 欧美另类亚洲清纯唯美| 少妇高潮的动态图| 亚洲一级一片aⅴ在线观看| 日韩高清综合在线| 国产免费又黄又爽又色| 久久久午夜欧美精品| 中文字幕熟女人妻在线| 男人狂女人下面高潮的视频| 欧美激情久久久久久爽电影| 草草在线视频免费看| 性插视频无遮挡在线免费观看| 欧美成人午夜免费资源| 精品熟女少妇av免费看| 综合色av麻豆| 中文字幕制服av| 伦理电影大哥的女人| 搡女人真爽免费视频火全软件| 国产成人a∨麻豆精品| 亚洲图色成人| 小说图片视频综合网站| 国产av不卡久久| 国产精品久久久久久精品电影小说 | 久久精品夜夜夜夜夜久久蜜豆| 91久久精品国产一区二区成人| 精品99又大又爽又粗少妇毛片| 波野结衣二区三区在线| 久久久久久久久久黄片| 99热6这里只有精品| 国产亚洲精品av在线| 只有这里有精品99| 99久久无色码亚洲精品果冻| 中文在线观看免费www的网站| 日本免费在线观看一区| 狂野欧美激情性xxxx在线观看| 99热全是精品| 精品少妇黑人巨大在线播放 | 又黄又爽又刺激的免费视频.| 日本黄色视频三级网站网址| 有码 亚洲区| 日韩一区二区三区影片| 性插视频无遮挡在线免费观看| 狠狠狠狠99中文字幕| 一卡2卡三卡四卡精品乱码亚洲| 国产探花极品一区二区| 欧美一区二区精品小视频在线| 婷婷色综合大香蕉| 99久久中文字幕三级久久日本| 久久99热这里只频精品6学生 | 一级爰片在线观看| 日韩在线高清观看一区二区三区| 日日摸夜夜添夜夜添av毛片| 97热精品久久久久久| 国产亚洲精品久久久com| 亚洲av电影不卡..在线观看| 国产精品99久久久久久久久| 又粗又爽又猛毛片免费看| 精品久久久久久久久av| 欧美成人一区二区免费高清观看| 免费在线观看成人毛片| 又黄又爽又刺激的免费视频.| 国产白丝娇喘喷水9色精品| 亚洲成人精品中文字幕电影| 高清av免费在线| av视频在线观看入口| 三级毛片av免费| 亚洲丝袜综合中文字幕| 你懂的网址亚洲精品在线观看 | 日本av手机在线免费观看| 狂野欧美白嫩少妇大欣赏| 啦啦啦观看免费观看视频高清| 国产一级毛片七仙女欲春2| 亚洲国产欧洲综合997久久,| 插阴视频在线观看视频| 亚洲精品乱码久久久v下载方式| 日本色播在线视频| 国产伦精品一区二区三区视频9| 免费看美女性在线毛片视频| 青春草国产在线视频| 日韩欧美在线乱码| 亚洲精品乱码久久久久久按摩| 国产精品日韩av在线免费观看| 99在线视频只有这里精品首页| 国产色婷婷99| 九九在线视频观看精品| 亚洲国产精品专区欧美| 国产精品久久视频播放| 蜜桃久久精品国产亚洲av| 高清视频免费观看一区二区 | 久久久久久久久久久免费av| 精品人妻视频免费看| 国产精品一区二区三区四区久久| 好男人视频免费观看在线| 蜜桃久久精品国产亚洲av| 九九在线视频观看精品| 久久久久久久久大av| 欧美成人一区二区免费高清观看| 高清在线视频一区二区三区 | 国产精品人妻久久久影院| 亚洲五月天丁香| 级片在线观看| 日本-黄色视频高清免费观看| av免费在线看不卡| 亚洲婷婷狠狠爱综合网| 国产精品永久免费网站| 色网站视频免费| 国产精品福利在线免费观看| 深夜a级毛片| 久久人人爽人人爽人人片va| 99在线人妻在线中文字幕| av国产久精品久网站免费入址| 91精品国产九色| 熟妇人妻久久中文字幕3abv| 男的添女的下面高潮视频| 七月丁香在线播放| 亚洲av男天堂| 日日撸夜夜添| 久久这里只有精品中国| 综合色丁香网| 男女边吃奶边做爰视频| 色综合亚洲欧美另类图片| 2021天堂中文幕一二区在线观| 成年女人看的毛片在线观看| 久久久久久久久久久免费av| 国产私拍福利视频在线观看| av在线天堂中文字幕| 超碰av人人做人人爽久久| 日韩欧美精品免费久久| 最后的刺客免费高清国语| 99热这里只有是精品在线观看| 久久精品国产亚洲av涩爱| 日本黄大片高清| 婷婷色综合大香蕉| 久久久久久伊人网av| 97超视频在线观看视频| 欧美激情在线99| 免费在线观看成人毛片| 中文资源天堂在线| 精品一区二区三区人妻视频| 久久精品国产自在天天线| 国产精品爽爽va在线观看网站| 91久久精品国产一区二区成人| 晚上一个人看的免费电影| 亚洲精品456在线播放app| 99热这里只有是精品50| 国产精品野战在线观看| 国产成人精品一,二区| 女人被狂操c到高潮| 99久久精品国产国产毛片| 免费大片18禁| 99在线人妻在线中文字幕| 午夜a级毛片| 日产精品乱码卡一卡2卡三| av.在线天堂| 人妻制服诱惑在线中文字幕| 欧美潮喷喷水| 高清毛片免费看| 干丝袜人妻中文字幕| 久久久国产成人免费| 少妇裸体淫交视频免费看高清| 国产国拍精品亚洲av在线观看| 成人三级黄色视频| 国内少妇人妻偷人精品xxx网站| 在线观看66精品国产| 国产亚洲精品av在线| 最近最新中文字幕大全电影3| 男人狂女人下面高潮的视频| 国产真实乱freesex| 日韩欧美精品免费久久| 欧美zozozo另类| 亚洲av一区综合| 国产视频首页在线观看| 五月玫瑰六月丁香| 99久久精品热视频| 精华霜和精华液先用哪个| 久久欧美精品欧美久久欧美| 亚洲国产高清在线一区二区三| 日韩,欧美,国产一区二区三区 | 汤姆久久久久久久影院中文字幕 | 日韩视频在线欧美| 国产精品伦人一区二区| 久久人人爽人人片av| 在线免费十八禁| 一本一本综合久久| 一边摸一边抽搐一进一小说| 狠狠狠狠99中文字幕| 免费观看人在逋| 国产亚洲午夜精品一区二区久久 | 啦啦啦啦在线视频资源| 亚洲欧美中文字幕日韩二区| 午夜免费激情av| 天天躁日日操中文字幕| 午夜亚洲福利在线播放| 中文字幕熟女人妻在线| 国产成人福利小说| 黑人高潮一二区| av免费在线看不卡| 久久久久久久久大av| 菩萨蛮人人尽说江南好唐韦庄 | 99在线人妻在线中文字幕| 黑人高潮一二区| 国产精品国产三级国产av玫瑰| 精品国产三级普通话版| 黄片wwwwww| 亚洲精品日韩av片在线观看| 久久99蜜桃精品久久| 村上凉子中文字幕在线| 亚洲精品久久久久久婷婷小说 | 九九久久精品国产亚洲av麻豆| 少妇被粗大猛烈的视频| 欧美bdsm另类| 久久久久精品久久久久真实原创| 哪个播放器可以免费观看大片| 日本色播在线视频| 波多野结衣高清无吗| 日韩av在线免费看完整版不卡| 国产一级毛片七仙女欲春2| 亚洲中文字幕一区二区三区有码在线看| 亚洲中文字幕日韩| 亚洲中文字幕日韩| 精品熟女少妇av免费看| 亚洲av日韩在线播放| 国产女主播在线喷水免费视频网站 | 久久精品综合一区二区三区| 精品午夜福利在线看| 亚洲国产欧美在线一区| 亚洲av福利一区| 日韩欧美三级三区| 亚洲av福利一区| 色哟哟·www| 听说在线观看完整版免费高清| 乱人视频在线观看| 国产精品伦人一区二区| 欧美一级a爱片免费观看看| 免费观看精品视频网站| 国产伦精品一区二区三区四那| 亚洲电影在线观看av| 亚洲自偷自拍三级| 禁无遮挡网站| 中文字幕久久专区| 国产三级中文精品| 一个人观看的视频www高清免费观看| 国产在视频线在精品| 麻豆久久精品国产亚洲av| 日韩欧美三级三区| 免费观看人在逋| 日韩 亚洲 欧美在线| 国产又黄又爽又无遮挡在线| 免费av不卡在线播放| 久久99蜜桃精品久久| 久久久久久国产a免费观看| 成人av在线播放网站| 尾随美女入室| 久久精品久久久久久噜噜老黄 | av在线亚洲专区| 国产精品三级大全| 成人午夜精彩视频在线观看| 精品久久久久久久久av| 看片在线看免费视频| 五月玫瑰六月丁香| 亚洲三级黄色毛片| 日本猛色少妇xxxxx猛交久久| 99热这里只有是精品50| 国产男人的电影天堂91| 又黄又爽又刺激的免费视频.| 亚洲伊人久久精品综合 | 亚洲精品久久久久久婷婷小说 | 97超碰精品成人国产| 亚洲自拍偷在线| 午夜亚洲福利在线播放| 一卡2卡三卡四卡精品乱码亚洲| 色综合色国产| 一区二区三区四区激情视频| 久久久久国产网址| 91久久精品国产一区二区成人| 国产成人一区二区在线| 国产黄色小视频在线观看| 久久久久免费精品人妻一区二区| 美女xxoo啪啪120秒动态图| 波多野结衣巨乳人妻| 国产精品三级大全| 午夜免费男女啪啪视频观看| a级毛片免费高清观看在线播放| 久久这里有精品视频免费| АⅤ资源中文在线天堂| 中文字幕久久专区| 色哟哟·www| 一级毛片aaaaaa免费看小| 国产成人精品婷婷| 啦啦啦韩国在线观看视频| 亚洲国产欧洲综合997久久,| 99热精品在线国产| 欧美性猛交黑人性爽| 赤兔流量卡办理| a级毛片免费高清观看在线播放| 精品久久久久久久人妻蜜臀av| 国产av不卡久久| 久久欧美精品欧美久久欧美| 日本欧美国产在线视频| 大话2 男鬼变身卡| 麻豆成人午夜福利视频| 亚洲精品自拍成人| 人妻系列 视频| 午夜日本视频在线| 国产精品美女特级片免费视频播放器| 国产精品综合久久久久久久免费| 人妻少妇偷人精品九色| 欧美又色又爽又黄视频| 又爽又黄无遮挡网站| 精品人妻视频免费看| 亚洲精品456在线播放app| 亚洲精品日韩av片在线观看| 99久国产av精品| 3wmmmm亚洲av在线观看| 久久精品国产鲁丝片午夜精品| 亚洲av成人精品一二三区| 久久精品国产亚洲av天美| 亚洲欧美成人综合另类久久久 | 床上黄色一级片| 成人一区二区视频在线观看| 国产精品日韩av在线免费观看| 亚洲人成网站高清观看| 日韩欧美精品v在线| 欧美激情在线99| 91精品一卡2卡3卡4卡| 黑人高潮一二区| 成人鲁丝片一二三区免费| 亚洲经典国产精华液单| 激情 狠狠 欧美| 欧美成人精品欧美一级黄| 午夜福利在线在线| 久久久国产成人精品二区| 亚洲精品成人久久久久久| 中文字幕免费在线视频6| 草草在线视频免费看| 又爽又黄a免费视频| 在线免费观看的www视频| 欧美97在线视频| 久久欧美精品欧美久久欧美| 秋霞在线观看毛片| 91aial.com中文字幕在线观看| 99久国产av精品| 亚洲欧美成人综合另类久久久 | 国产一区亚洲一区在线观看| 一级毛片aaaaaa免费看小| 欧美性猛交黑人性爽| 18+在线观看网站| 在线观看美女被高潮喷水网站| 国产午夜精品论理片| 搡老妇女老女人老熟妇| 看十八女毛片水多多多| 亚洲最大成人手机在线| ponron亚洲| 久久人人爽人人片av| 日韩欧美精品v在线| 午夜精品国产一区二区电影 | 99久久九九国产精品国产免费| 欧美xxxx黑人xx丫x性爽| 插逼视频在线观看| 乱系列少妇在线播放| 国产极品天堂在线| 国产精品蜜桃在线观看| 国产不卡一卡二| 久久精品国产亚洲av天美| 婷婷六月久久综合丁香| 国产免费又黄又爽又色| 我要看日韩黄色一级片| 久久鲁丝午夜福利片| 一区二区三区四区激情视频| 两性午夜刺激爽爽歪歪视频在线观看| 亚洲国产精品成人综合色| 精品国产露脸久久av麻豆 | 51国产日韩欧美| 日产精品乱码卡一卡2卡三| 久热久热在线精品观看| 亚洲伊人久久精品综合 | 亚洲综合色惰| 国产探花在线观看一区二区| 久久综合国产亚洲精品| 人妻制服诱惑在线中文字幕| 亚洲国产最新在线播放| 精品人妻偷拍中文字幕| 99久国产av精品| 免费av毛片视频| 99热精品在线国产| 视频中文字幕在线观看| 亚洲成色77777| 天堂中文最新版在线下载 | 亚洲一级一片aⅴ在线观看| 国产精品电影一区二区三区| 午夜亚洲福利在线播放| 国产av码专区亚洲av| 精品一区二区三区视频在线| 欧美日韩在线观看h| 高清毛片免费看| 日韩欧美三级三区| 日韩在线高清观看一区二区三区| 少妇猛男粗大的猛烈进出视频 | 亚洲经典国产精华液单| 国产淫片久久久久久久久| 蜜臀久久99精品久久宅男| 亚洲av不卡在线观看| 色综合亚洲欧美另类图片| 联通29元200g的流量卡| 校园人妻丝袜中文字幕| 国产精品久久久久久久电影| 99热全是精品| 亚洲av成人精品一二三区| 日韩视频在线欧美| 少妇裸体淫交视频免费看高清| 免费av不卡在线播放| av女优亚洲男人天堂| 只有这里有精品99| 高清在线视频一区二区三区 | 老女人水多毛片| 国产黄a三级三级三级人| 国产午夜精品论理片| 午夜福利在线观看吧| 久久久久久九九精品二区国产| 久久精品国产99精品国产亚洲性色| 99热网站在线观看| 久久久久久久久中文| 日日干狠狠操夜夜爽| 免费观看在线日韩| 麻豆av噜噜一区二区三区| 精品久久久久久电影网 | 久久6这里有精品| 熟女电影av网| 建设人人有责人人尽责人人享有的 | 亚洲av熟女| 国产午夜福利久久久久久| 国产中年淑女户外野战色| 国产不卡一卡二| 夜夜爽夜夜爽视频| 嫩草影院新地址| 亚洲电影在线观看av| a级毛色黄片| 国产成人91sexporn| 久久这里有精品视频免费| av视频在线观看入口| 日本黄大片高清| 干丝袜人妻中文字幕| 免费电影在线观看免费观看| 97超碰精品成人国产| 亚洲高清免费不卡视频| 又粗又爽又猛毛片免费看| 亚洲久久久久久中文字幕| 久久精品影院6| 日本熟妇午夜| 国产片特级美女逼逼视频| 日本色播在线视频| 黄色欧美视频在线观看| 91在线精品国自产拍蜜月| 大话2 男鬼变身卡| 国产v大片淫在线免费观看| 五月玫瑰六月丁香| 伊人久久精品亚洲午夜| 午夜精品在线福利| 亚洲成人中文字幕在线播放| 免费观看精品视频网站| 欧美激情在线99| 久久久久久九九精品二区国产| 午夜福利成人在线免费观看| 麻豆国产97在线/欧美| 国产毛片a区久久久久| 成人av在线播放网站| 色噜噜av男人的天堂激情| 一级毛片aaaaaa免费看小| 99国产精品一区二区蜜桃av| 22中文网久久字幕| 别揉我奶头 嗯啊视频| 国产亚洲最大av| 亚洲成人av在线免费| 久久久久性生活片| 丰满人妻一区二区三区视频av| 91狼人影院| 大香蕉97超碰在线| 精华霜和精华液先用哪个| 成人一区二区视频在线观看| 一个人观看的视频www高清免费观看| 22中文网久久字幕| 亚洲国产精品久久男人天堂| 狂野欧美激情性xxxx在线观看| 欧美日韩综合久久久久久| 青春草国产在线视频| 日韩欧美三级三区| 免费看光身美女| 国产精品久久久久久精品电影小说 | 51国产日韩欧美| 色综合亚洲欧美另类图片| 日日摸夜夜添夜夜添av毛片| 国产 一区精品| 日本一本二区三区精品| 激情 狠狠 欧美| 欧美日韩一区二区视频在线观看视频在线 | 男女下面进入的视频免费午夜| 免费人成在线观看视频色| 久久韩国三级中文字幕| 久久欧美精品欧美久久欧美| 久久人妻av系列| 中国国产av一级| 99视频精品全部免费 在线| 99热这里只有是精品在线观看| 亚洲精品乱久久久久久| 国产成人freesex在线| 成人性生交大片免费视频hd| 午夜a级毛片| 国产在线男女| 欧美成人一区二区免费高清观看| 中文欧美无线码| 欧美97在线视频| 毛片一级片免费看久久久久| 蜜臀久久99精品久久宅男| 国产成人a区在线观看| 亚洲欧美日韩卡通动漫| 22中文网久久字幕| 国产在视频线在精品| 一个人免费在线观看电影| 成人毛片60女人毛片免费| 亚洲国产精品久久男人天堂| 一级二级三级毛片免费看| 三级国产精品欧美在线观看| 内地一区二区视频在线| 嫩草影院精品99| 欧美三级亚洲精品| 成人亚洲精品av一区二区| 国产国拍精品亚洲av在线观看| 亚洲成色77777| 欧美丝袜亚洲另类| 亚洲一级一片aⅴ在线观看| 国产精品乱码一区二三区的特点| 人妻少妇偷人精品九色| 日日撸夜夜添| 久久鲁丝午夜福利片| 中文字幕精品亚洲无线码一区| 久久久久久久久久黄片| 99久久九九国产精品国产免费| 麻豆乱淫一区二区| 亚洲av福利一区| .国产精品久久| 成人二区视频| 色综合站精品国产| 精品熟女少妇av免费看| 99热这里只有精品一区| 深爱激情五月婷婷| 欧美一区二区精品小视频在线| 级片在线观看|