Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer bas...Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.展开更多
The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To addr...The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To address this challenge,we developed a deep generative model aimed at synthesizing medical data to overcome data sharing barriers,with a focus on breast ultrasound(US)image synthesis.Specifically,we introduce CoLDiT,a conditional latent diffusion model with a transformer backbone,to generate US images of breast lesions across various Breast Imaging Reporting and Data System(BI-RADS)categories.Using a training dataset of 9,705 US images from 5,243 patients across 202 hospitals with diverse US systems,CoLDiT generated breast US images without duplicating private information,as confirmed through nearest-neighbor analysis.Blinded reader studies further validated the realism of these images,with area under the receiver operating characteristic curve(AUC)scores ranging from 0.53 to 0.77.Additionally,synthetic breast US images effectively augmented the training dataset for BI-RADS classification,achieving performance comparable to that using an equal-sized training set comprising solely real images(P=0.81 for AUC).Our findings suggest that synthetic data,such as CoLDiT-generated images,offer a viable,privacy-preserving solution to facilitate secure medical data sharing and advance the utilization of medical big data.展开更多
文摘Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.
基金supported by the National Natural Science Foundation of China(grant no.82071928)the Program of Shanghai Academic/Technology Research Leader(grant no.23XD1401300).
文摘The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To address this challenge,we developed a deep generative model aimed at synthesizing medical data to overcome data sharing barriers,with a focus on breast ultrasound(US)image synthesis.Specifically,we introduce CoLDiT,a conditional latent diffusion model with a transformer backbone,to generate US images of breast lesions across various Breast Imaging Reporting and Data System(BI-RADS)categories.Using a training dataset of 9,705 US images from 5,243 patients across 202 hospitals with diverse US systems,CoLDiT generated breast US images without duplicating private information,as confirmed through nearest-neighbor analysis.Blinded reader studies further validated the realism of these images,with area under the receiver operating characteristic curve(AUC)scores ranging from 0.53 to 0.77.Additionally,synthetic breast US images effectively augmented the training dataset for BI-RADS classification,achieving performance comparable to that using an equal-sized training set comprising solely real images(P=0.81 for AUC).Our findings suggest that synthetic data,such as CoLDiT-generated images,offer a viable,privacy-preserving solution to facilitate secure medical data sharing and advance the utilization of medical big data.
