Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substan...Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substantial user variation.To enhance the efficiency and consistency,we developed a unified deep learning(DL)OAR contouring model,DeepOAR,that was trained using multiple partially labeled datasets for segmenting a comprehensive set of thoracic OARs following the Radiation Therapy Oncology Group(RTOG)-guided OAR atlas.This DL model supports the segmentation of six required and eight optional OARs guided by the NRG-RTOG 1106 trial,providing precise and reproducible OARs contouring that are ready to be used in radiotherapy practice.Materials and methods:Following the OAR contouring recommendation of the NRG-RTOG 1106 trial,we collected and curated three private datasets and two public datasets,comprising a total of 531 patients with partially annotated thoracic OARs.These partially annotated datasets were utilized to develop DeepOAR,which consisted of a shared encoder and 14 separate decoders,with each decoder dedicated to one specific OAR.For model training,we utilized all patients from the two public datasets and 75%of the patients from the private datasets.We reserved the remaining 25%of the private datasets for independent testing.A multi-user study involving 21 radiation oncologists was conducted on 40 randomly selected patients from the independent testing dataset to evaluate the clinical applicability of DeepOAR.The Dice coefficient score(DSC)and average surface distance(ASD)were computed to evaluate the quantitative delineation performance of the model.Results:DeepOAR outperformed nnUNet(the benchmark medical segmentation model)across all 14 OARs,achieving mean DSC and ASD values of 88.4%and 1.0 mm,respectively,in the independent testing set.Multi-user validation demonstrated that 89.7%of DeepOAR-generated OARs were clinically acceptable or required only minor revisions.A comparison using two randomly selected patients showed that the delineation variability of DeepOAR was significantly smaller than the inter-user variation among radiation oncologists.Human editing of DeepOAR’s predictions could further improve OAR delineation accuracy by an average of 3%increase in DSC and 40%reduction in ASD while significantly reducing the workload of radiation oncologists for contouring 14 thoracic OARs by an average of 77.0%.Conclusion:We developed DeepOAR,a DL-based unified contouring model trained using multiple partially labeled datasets,to delineate a comprehensive set of 14 thoracic OARs following the RTOG-guided OAR atlas.Both qualitative and quantitative results demonstrated the strong clinical applicability of DeepOAR for the OAR delineation process in thoracic cancer radiotherapy workflows,along with improved efficiency,comprehensiveness,and quality.展开更多
Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled da...Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled dataset construction is the basis.Android occupies a huge share of the mobile operating system market.Instant Messaging(IM)applications are important tools for people communication.But such applications have complex functions which frequently switched,so it is difficult to obtain function-level labels.The existing function-level public datasets in Android are rare and noisy,leading to research stagnation.Most labeled samples are collected with WLAN devices,which cannot exclude the operating system background traffic.At the same time,other datasets need to obtain root permission or use scripts to simulate user behavior.These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained.Previous work(Chen et al.in Appl Sci 12(22):11731,2022)proposed a one-stop automated encrypted traffic labeled sample collection,construction,and correlation system,A3C,running at the application-level in Android.This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android,F3L.The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground.It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions.The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels.Experiments show that the method can match the correct label within 3 s after the user operation.展开更多
Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study pr...Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.展开更多
基金Xianghua Ye,Zhejiang Provincial Science and Technology Project,2024-KY1-001-105.This funding is intended to support the training of organ auto-segmentation models.
文摘Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substantial user variation.To enhance the efficiency and consistency,we developed a unified deep learning(DL)OAR contouring model,DeepOAR,that was trained using multiple partially labeled datasets for segmenting a comprehensive set of thoracic OARs following the Radiation Therapy Oncology Group(RTOG)-guided OAR atlas.This DL model supports the segmentation of six required and eight optional OARs guided by the NRG-RTOG 1106 trial,providing precise and reproducible OARs contouring that are ready to be used in radiotherapy practice.Materials and methods:Following the OAR contouring recommendation of the NRG-RTOG 1106 trial,we collected and curated three private datasets and two public datasets,comprising a total of 531 patients with partially annotated thoracic OARs.These partially annotated datasets were utilized to develop DeepOAR,which consisted of a shared encoder and 14 separate decoders,with each decoder dedicated to one specific OAR.For model training,we utilized all patients from the two public datasets and 75%of the patients from the private datasets.We reserved the remaining 25%of the private datasets for independent testing.A multi-user study involving 21 radiation oncologists was conducted on 40 randomly selected patients from the independent testing dataset to evaluate the clinical applicability of DeepOAR.The Dice coefficient score(DSC)and average surface distance(ASD)were computed to evaluate the quantitative delineation performance of the model.Results:DeepOAR outperformed nnUNet(the benchmark medical segmentation model)across all 14 OARs,achieving mean DSC and ASD values of 88.4%and 1.0 mm,respectively,in the independent testing set.Multi-user validation demonstrated that 89.7%of DeepOAR-generated OARs were clinically acceptable or required only minor revisions.A comparison using two randomly selected patients showed that the delineation variability of DeepOAR was significantly smaller than the inter-user variation among radiation oncologists.Human editing of DeepOAR’s predictions could further improve OAR delineation accuracy by an average of 3%increase in DSC and 40%reduction in ASD while significantly reducing the workload of radiation oncologists for contouring 14 thoracic OARs by an average of 77.0%.Conclusion:We developed DeepOAR,a DL-based unified contouring model trained using multiple partially labeled datasets,to delineate a comprehensive set of 14 thoracic OARs following the RTOG-guided OAR atlas.Both qualitative and quantitative results demonstrated the strong clinical applicability of DeepOAR for the OAR delineation process in thoracic cancer radiotherapy workflows,along with improved efficiency,comprehensiveness,and quality.
基金supported by the General Program of the National Natural Science Foundation of China under Grant No.62172093.
文摘Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled dataset construction is the basis.Android occupies a huge share of the mobile operating system market.Instant Messaging(IM)applications are important tools for people communication.But such applications have complex functions which frequently switched,so it is difficult to obtain function-level labels.The existing function-level public datasets in Android are rare and noisy,leading to research stagnation.Most labeled samples are collected with WLAN devices,which cannot exclude the operating system background traffic.At the same time,other datasets need to obtain root permission or use scripts to simulate user behavior.These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained.Previous work(Chen et al.in Appl Sci 12(22):11731,2022)proposed a one-stop automated encrypted traffic labeled sample collection,construction,and correlation system,A3C,running at the application-level in Android.This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android,F3L.The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground.It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions.The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels.Experiments show that the method can match the correct label within 3 s after the user operation.
文摘Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.
