Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteri...Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteristics in traditional Chinese medicine(TCM)inspection,thereby addressing the critical challenge of privacy preservation in medical image analysis.Methods A facial image dataset was constructed from participants at Nanjing University of Chinese Medicine between April 23 and June 10,2023,using a TCM full-body inspection data acquisition equipment under controlled illumination.The proposed FCP-GAN model was designed to achieve the dual objectives of removing identity features and preserving colors through three key components:(i)a multi-space combination module that comprehensively extracts color attributes from red,green,blue(RGB),hue,saturation,value(HSV),and Lab spaces;(ii)a generator incorporating efficient channel attention(ECA)mechanism to enhance the representation of diagnostically critical color channels;and(iii)a dual-loss function that combines adversarial loss for de-identification with a dedicated color preservation loss.The model was trained and evaluated using a stratified 5-fold cross-validation strategy and evaluated against four baseline generative models:conditional GAN(CGAN),deep convolutional GAN(DCGAN),dual discriminator CGAN(DDCGAN),and medical GAN(MedGAN).Performance was assessed in terms of image quality[peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)],distribution similarity[Fréchet inception distance(FID)],privacy protection(face recognition accuracy),and diagnostic consistency[mean squared error(MSE)and Pearson correlation coefficient(PCC)].Results The final analysis included facial images from 216 participants.Compared with baseline models,FCP-GAN achieved superior performance,with PSNR=31.02 dB and SSIM=0.908,representing an improvement of 1.21 dB and 0.034 in SSIM over the strongest baseline(MedGAN).The FID value(23.45)was also the lowest among all models,indicating superior distributional similarity to real images.The multi-space feature fusion and the ECA mechanism contributed significantly to these performance gains,as evidenced by ablation studies.The stratified 5-fold cross-validation confirmed the model’s robustness,with results reported as mean±standard deviation(SD)across all folds.The model effectively protected privacy by reducing face recognition accuracy from 95.2%(original images)to 60.1%(generated images).Critically,it maintained high diagnostic fidelity,as evidenced by a low MSE(<0.051)and a high PCC(>0.98)for key TCM facial features between original and generated images.Conclusion The FCP-GAN model provides an effective technical solution for ensuring privacy in TCM diagnostic imaging,successfully having removed identity features while preserving clinically vital facial color features.This study offers significant value for developing intelligent and secure TCM telemedicine systems.展开更多
目的:研究早期股骨头坏死(osteonecrosis of the femoral head,ONFH)患者面色及舌脉信息的分布特征。方法:采用中医四诊仪采集81例ONFH患者(观察组)和81例健康成年人(对照组)的面色及舌脉信息,分析两组间差异及特异性表现。结果:两组面...目的:研究早期股骨头坏死(osteonecrosis of the femoral head,ONFH)患者面色及舌脉信息的分布特征。方法:采用中医四诊仪采集81例ONFH患者(观察组)和81例健康成年人(对照组)的面色及舌脉信息,分析两组间差异及特异性表现。结果:两组面色及唇色比较,差异无统计学意义(P>0.05);面部光泽度比较,差异有统计学意义(P<0.05)。两组舌色比较,差异无统计学意义(P>0.05);舌体胖瘦、有无齿痕及点刺两组比较差异无统计学意义(P>0.05),有无裂纹两组比较差异有统计学意义(P<0.001),舌形各要素汇总后两组比较差异有统计学意义(P<0.001);两组舌苔颜色比较,差异有统计学意义(P<0.05),舌苔质地中,厚薄、有无腻苔比较差异有统计学意义(P<0.001),有无腐苔、剥脱苔比较差异无统计学意义(P>0.05),苔质各要素汇总后比较差异有统计学意义(P<0.001)。两组脉位、脉率、脉律、紧张度及流利度比较,差异均有统计学意义(P<0.05);脉力比较,差异无统计学意义(P>0.05)。结论:早期ONFH患者面色以光泽偏少为著;舌象多为舌形胖大(伴或不伴裂纹),舌苔黄或黄白相兼且多厚腻;脉象以脉位略沉、脉率偏数,促脉、代脉、结脉及弦脉、滑脉增多为特点。展开更多
目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5...目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5皮肤性质测试仪进行定量分析和评价。结果:VI SI A数据显示色素斑、皱纹、纹理和毛孔治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后和8次治疗后紫质均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。SOFT数据显示水分和弹性治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后油脂有明显差异性(P<0.05),与治疗前比较8次治疗后pH和油脂均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。结论:红蓝光对痤疮患者皮肤作用效应主要表现为油脂分泌减少,短期改变皮肤pH,但不会影响皮肤色素及水分。展开更多
基金National Key Research and Development Program of China(2022YFC3502302)Graduate Research Innovation Program of Jiangsu Province(KYCX25_2269)。
文摘Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteristics in traditional Chinese medicine(TCM)inspection,thereby addressing the critical challenge of privacy preservation in medical image analysis.Methods A facial image dataset was constructed from participants at Nanjing University of Chinese Medicine between April 23 and June 10,2023,using a TCM full-body inspection data acquisition equipment under controlled illumination.The proposed FCP-GAN model was designed to achieve the dual objectives of removing identity features and preserving colors through three key components:(i)a multi-space combination module that comprehensively extracts color attributes from red,green,blue(RGB),hue,saturation,value(HSV),and Lab spaces;(ii)a generator incorporating efficient channel attention(ECA)mechanism to enhance the representation of diagnostically critical color channels;and(iii)a dual-loss function that combines adversarial loss for de-identification with a dedicated color preservation loss.The model was trained and evaluated using a stratified 5-fold cross-validation strategy and evaluated against four baseline generative models:conditional GAN(CGAN),deep convolutional GAN(DCGAN),dual discriminator CGAN(DDCGAN),and medical GAN(MedGAN).Performance was assessed in terms of image quality[peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)],distribution similarity[Fréchet inception distance(FID)],privacy protection(face recognition accuracy),and diagnostic consistency[mean squared error(MSE)and Pearson correlation coefficient(PCC)].Results The final analysis included facial images from 216 participants.Compared with baseline models,FCP-GAN achieved superior performance,with PSNR=31.02 dB and SSIM=0.908,representing an improvement of 1.21 dB and 0.034 in SSIM over the strongest baseline(MedGAN).The FID value(23.45)was also the lowest among all models,indicating superior distributional similarity to real images.The multi-space feature fusion and the ECA mechanism contributed significantly to these performance gains,as evidenced by ablation studies.The stratified 5-fold cross-validation confirmed the model’s robustness,with results reported as mean±standard deviation(SD)across all folds.The model effectively protected privacy by reducing face recognition accuracy from 95.2%(original images)to 60.1%(generated images).Critically,it maintained high diagnostic fidelity,as evidenced by a low MSE(<0.051)and a high PCC(>0.98)for key TCM facial features between original and generated images.Conclusion The FCP-GAN model provides an effective technical solution for ensuring privacy in TCM diagnostic imaging,successfully having removed identity features while preserving clinically vital facial color features.This study offers significant value for developing intelligent and secure TCM telemedicine systems.
文摘目的:研究早期股骨头坏死(osteonecrosis of the femoral head,ONFH)患者面色及舌脉信息的分布特征。方法:采用中医四诊仪采集81例ONFH患者(观察组)和81例健康成年人(对照组)的面色及舌脉信息,分析两组间差异及特异性表现。结果:两组面色及唇色比较,差异无统计学意义(P>0.05);面部光泽度比较,差异有统计学意义(P<0.05)。两组舌色比较,差异无统计学意义(P>0.05);舌体胖瘦、有无齿痕及点刺两组比较差异无统计学意义(P>0.05),有无裂纹两组比较差异有统计学意义(P<0.001),舌形各要素汇总后两组比较差异有统计学意义(P<0.001);两组舌苔颜色比较,差异有统计学意义(P<0.05),舌苔质地中,厚薄、有无腻苔比较差异有统计学意义(P<0.001),有无腐苔、剥脱苔比较差异无统计学意义(P>0.05),苔质各要素汇总后比较差异有统计学意义(P<0.001)。两组脉位、脉率、脉律、紧张度及流利度比较,差异均有统计学意义(P<0.05);脉力比较,差异无统计学意义(P>0.05)。结论:早期ONFH患者面色以光泽偏少为著;舌象多为舌形胖大(伴或不伴裂纹),舌苔黄或黄白相兼且多厚腻;脉象以脉位略沉、脉率偏数,促脉、代脉、结脉及弦脉、滑脉增多为特点。
文摘目的:定量评价红蓝光对痤疮患者面部皮肤的作用效应。方法:随机选取2010年6月～2011年6月在笔者激光美容中心进行治疗的痤疮患者150例,应用红蓝光交替照射,2次/周,共4周,于治疗前、4次治疗后和8次治疗后用VISIA皮肤图像分析仪和SOFT5.5皮肤性质测试仪进行定量分析和评价。结果:VI SI A数据显示色素斑、皱纹、纹理和毛孔治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后和8次治疗后紫质均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。SOFT数据显示水分和弹性治疗前后未见明显差异性(P>0.05);与治疗前比较4次治疗后油脂有明显差异性(P<0.05),与治疗前比较8次治疗后pH和油脂均有明显差异性(P<0.05),但治疗后4次和8次之间未见明显差异性(P>0.05)。结论:红蓝光对痤疮患者皮肤作用效应主要表现为油脂分泌减少,短期改变皮肤pH,但不会影响皮肤色素及水分。
