The estimation of image resampling factors is an important problem in image forensics.Among all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted...The estimation of image resampling factors is an important problem in image forensics.Among all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest.However,because of inherent ambiguity,spectrum-based methods fail to discriminate upscale and downscale operations without any prior information.In general,the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image.Firstly,the resampling process will introduce correlations between neighboring pixels.In this case,a set of periodic pixels that are correlated to their neighbors can be found in a resampled image.Secondly,the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks.Hence,in this paper,we propose a dual-stream convolutional neural network for image resampling factors estimation.One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images.The other is frequency stream that discovers the differences of spectrum between rescaled and original images.The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain,which is later fed into softmax layer for resampling factor estimation.Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.展开更多
Background:Early recurrence is common for hepatocellular carcinoma(HCC)after surgical resection,being the leading cause of death.Traditionally,the COX proportional hazard(CPH)models based on linearity assumption have ...Background:Early recurrence is common for hepatocellular carcinoma(HCC)after surgical resection,being the leading cause of death.Traditionally,the COX proportional hazard(CPH)models based on linearity assumption have been used to predict early recurrence,but predictive performance is limited.Machine learning models offer a novel methodology and have several advantages over CPH models.Hence,the purpose of this study was to compare random survival forests(RSF)model with CPH models in prediction of early recurrence for HCC patients after curative resection.Methods:A total of 4,758 patients undergoing curative resection from two medical centers were included.Fifteen features including age,gender,etiology,platelet count,albumin,total bilirubin,AFP,tumor size,tumor number,microvascular invasion,macrovascular invasion,Edmondson-Steiner grade,tumor capsular,satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort.Discrimination,calibration,clinical usefulness and overall performance were assessed and compared with other models.Results:Five hundred survival trees were used to generate the RFS model.The five highest Variable Importance(VIMP)were tumor size,macrovascular invasion,microvascular invasion,tumor number and AFP.In training,internal and external validation cohort,the C-index of RSF model were 0.725[standard errors(SE)=0.005],0.762(SE=0.011)and 0.747(SE=0.016),respectively;the Gönen&Heller’s K of RSF model were 0.684(SE=0.005),0.711(SE=0.008)and 0.697(SE=0.014),respectively;the time-dependent AUC(2 years)of RSF model were 0.818(SE=0.008),0.823(SE=0.014)and 0.785(SE=0.025),respectively.The RSF model outperformed early recurrence after surgery for liver tumor(ERASL)model,Korean model,American Joint Committee on Cancer tumor-node-metastasis(AJCC TNM)stage,Barcelona Clinic Liver Cancer(BCLC)stage and Chinese stage.The RSF model is capable of stratifying patients into three different risk groups(low-risk,intermediate-risk,high-risk groups)in the training and two validation cohorts(all P<0.0001).A web-based prediction tool was built to facilitate clinical application(https://recurrenceprediction.shinyapps.io/surgery_predict/).Conclusions:The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models.This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.展开更多
Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours a...Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours and use the personal medical history for human identification.In this article,medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced,and were applied in the field of forensic identification of human images,which we called medical forensic identification of human images(mFIHI).The proposed method analysed the people’s medical signs by studying the appearance and behaviour characteristics depicted in images or videos,and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects.Through a conformity and difference analysis on medical indicators and their indicated diseases,it would provide an important information for human identification from images or videos.A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI,and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.展开更多
基金the National Natural Science Foundation of China(No.62072480)the Key Areas R&D Program of Guangdong(No.2019B010136002)the Key ScientificResearch Program of Guangzhou(No.201804020068).
文摘The estimation of image resampling factors is an important problem in image forensics.Among all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest.However,because of inherent ambiguity,spectrum-based methods fail to discriminate upscale and downscale operations without any prior information.In general,the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image.Firstly,the resampling process will introduce correlations between neighboring pixels.In this case,a set of periodic pixels that are correlated to their neighbors can be found in a resampled image.Secondly,the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks.Hence,in this paper,we propose a dual-stream convolutional neural network for image resampling factors estimation.One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images.The other is frequency stream that discovers the differences of spectrum between rescaled and original images.The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain,which is later fed into softmax layer for resampling factor estimation.Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.
基金supported by the Special Fund of Fujian Development and Reform Commission(31010308)the Natural Science Foundation of Fujian Province(2018J01140).
文摘Background:Early recurrence is common for hepatocellular carcinoma(HCC)after surgical resection,being the leading cause of death.Traditionally,the COX proportional hazard(CPH)models based on linearity assumption have been used to predict early recurrence,but predictive performance is limited.Machine learning models offer a novel methodology and have several advantages over CPH models.Hence,the purpose of this study was to compare random survival forests(RSF)model with CPH models in prediction of early recurrence for HCC patients after curative resection.Methods:A total of 4,758 patients undergoing curative resection from two medical centers were included.Fifteen features including age,gender,etiology,platelet count,albumin,total bilirubin,AFP,tumor size,tumor number,microvascular invasion,macrovascular invasion,Edmondson-Steiner grade,tumor capsular,satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort.Discrimination,calibration,clinical usefulness and overall performance were assessed and compared with other models.Results:Five hundred survival trees were used to generate the RFS model.The five highest Variable Importance(VIMP)were tumor size,macrovascular invasion,microvascular invasion,tumor number and AFP.In training,internal and external validation cohort,the C-index of RSF model were 0.725[standard errors(SE)=0.005],0.762(SE=0.011)and 0.747(SE=0.016),respectively;the Gönen&Heller’s K of RSF model were 0.684(SE=0.005),0.711(SE=0.008)and 0.697(SE=0.014),respectively;the time-dependent AUC(2 years)of RSF model were 0.818(SE=0.008),0.823(SE=0.014)and 0.785(SE=0.025),respectively.The RSF model outperformed early recurrence after surgery for liver tumor(ERASL)model,Korean model,American Joint Committee on Cancer tumor-node-metastasis(AJCC TNM)stage,Barcelona Clinic Liver Cancer(BCLC)stage and Chinese stage.The RSF model is capable of stratifying patients into three different risk groups(low-risk,intermediate-risk,high-risk groups)in the training and two validation cohorts(all P<0.0001).A web-based prediction tool was built to facilitate clinical application(https://recurrenceprediction.shinyapps.io/surgery_predict/).Conclusions:The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models.This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.
基金This work is supported by Shanghai Sailing Program[grant number 17YF1420000]Ministry of Finance of the People's Republic of China[grant numbers GY2018G-6 and GY2020G-8].
文摘Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours and use the personal medical history for human identification.In this article,medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced,and were applied in the field of forensic identification of human images,which we called medical forensic identification of human images(mFIHI).The proposed method analysed the people’s medical signs by studying the appearance and behaviour characteristics depicted in images or videos,and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects.Through a conformity and difference analysis on medical indicators and their indicated diseases,it would provide an important information for human identification from images or videos.A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI,and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.
