Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine tumors.The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures.T...Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine tumors.The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures.Therefore,we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma(PTC)diagnosis and characterize PTC characteristics.To alleviate any concerns pathologists may have about using the model,we conducted an analysis of the used bands that can be interpreted pathologically.A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients.The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue.Clinical data of the corresponding patients were collected for subsequent model interpretability analysis.The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University.The spectral preprocessing method was used to process the spectra,and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models.The PTC detection model using mean centering(MC)and multiple scattering correction(MSC)has optimal performance,and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide.For model interpretable analysis,the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak,and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients.Moreover,the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients.In addition,the correlation analysis was performed between the selected wavelengths and the clinical data,and the results show:the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure,nucleus size and free thyroxine(FT4),and has a strong correlation with triiodothyronine(T3);the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine(FT3).展开更多
Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing method...Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.展开更多
As the science and technology develop,crime methods and scenes have become increasingly complex and diverse.Trace evidence analysis has become amore and more important criminal investigation technology and liquid is t...As the science and technology develop,crime methods and scenes have become increasingly complex and diverse.Trace evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace evidence.Food can provide not only energy,but clues to solve crimes.In this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark colors.The obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data pretreatment.Subsequently,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed spectra.For modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification models.The experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food residuals.With respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification performance.In future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification models.This research may play a positive role in the construction of a harmonious society.展开更多
Industry and academia have been making great efforts in improving refresh rates and resolutions of display devices to meet the ever increasing needs of consumers for better visual quality.As a result,many modem displa...Industry and academia have been making great efforts in improving refresh rates and resolutions of display devices to meet the ever increasing needs of consumers for better visual quality.As a result,many modem displays have spatial and temporal resolutions far beyond the discern capability of human visual systems.Thus,leading to the possibility of using those display-eye redundancies for innovative usages.Tempo-ral/spatial psycho-visual modulation(TPVM/SPVM)was proposed to exploit those redundancies to generate multiple visual percepts for different viewers or to transmit non-visual data to computing devices without affecting normal viewing.This paper reviews the STPVM technology from both conceptual and algorithmic perspectives,with exemplary applications in multiview display,display with visible light communication,etc.Some possible future research directions are also identified.展开更多
基金supported by the grant awarded by the National Natural Science Foundation of China(No.62225112,No.61831015)the key research and development project of Anhui Province(No.202104j07020059).
文摘Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine tumors.The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures.Therefore,we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma(PTC)diagnosis and characterize PTC characteristics.To alleviate any concerns pathologists may have about using the model,we conducted an analysis of the used bands that can be interpreted pathologically.A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients.The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue.Clinical data of the corresponding patients were collected for subsequent model interpretability analysis.The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University.The spectral preprocessing method was used to process the spectra,and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models.The PTC detection model using mean centering(MC)and multiple scattering correction(MSC)has optimal performance,and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide.For model interpretable analysis,the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak,and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients.Moreover,the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients.In addition,the correlation analysis was performed between the selected wavelengths and the clinical data,and the results show:the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure,nucleus size and free thyroxine(FT4),and has a strong correlation with triiodothyronine(T3);the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine(FT3).
基金We would like to thank the participants of the CAS_palm set who consented to participate in research.This project was funded by the Shanghai Municipal Science and Technology Major Project 2017SHZDZX01(S.W.)National Natural Science Foundation of China Grant 61831015(G.Z.)China Postdoctoral Science Foundation Grant 2019M651351(J.L.).
文摘Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.
基金sponsored by the National Natural Science Foundation of China(No.61901172,No.61831015,No.U1908210)the Shanghai Sailing Program(No.19YF1414100)+3 种基金the“Chenguang Program”supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.19CG27)the Science and Technology Commission of Shanghai Municipality(No.19511120100,No.18DZ2270700,No.18DZ2270800)the foundation of Key Laboratory of Artificial Intelligence,Ministry of Education(No.AI2019002)and the Fundamental Research Funds for the Central Universities.
文摘As the science and technology develop,crime methods and scenes have become increasingly complex and diverse.Trace evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace evidence.Food can provide not only energy,but clues to solve crimes.In this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark colors.The obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data pretreatment.Subsequently,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed spectra.For modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification models.The experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food residuals.With respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification performance.In future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification models.This research may play a positive role in the construction of a harmonious society.
基金would like thanks the National Natural Science Foundation of China(NSFC)for the support(Grant Nos.61901259,61831015,61771305,61927809,and U1908210)China Postdoctoral Science Foundation(BX2019208)。
文摘Industry and academia have been making great efforts in improving refresh rates and resolutions of display devices to meet the ever increasing needs of consumers for better visual quality.As a result,many modem displays have spatial and temporal resolutions far beyond the discern capability of human visual systems.Thus,leading to the possibility of using those display-eye redundancies for innovative usages.Tempo-ral/spatial psycho-visual modulation(TPVM/SPVM)was proposed to exploit those redundancies to generate multiple visual percepts for different viewers or to transmit non-visual data to computing devices without affecting normal viewing.This paper reviews the STPVM technology from both conceptual and algorithmic perspectives,with exemplary applications in multiview display,display with visible light communication,etc.Some possible future research directions are also identified.