Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a n...Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images.One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images,inheriting the advantages of information-abundant MM polarimetric imaging.In this study,we develop a model using unpaired MM polarimetric images and bright-field images for generating standard hematoxylin and eosin(H&E)stained tissue images.Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images,the proposed Cycle-Consistent Generative Adversarial Networks(CycleGAN)-based model simplifies data acquisition and data preprocessing to a great extent.The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-field images as a viable approach,which provides an intuitive manner for pathologists for future polarization-assisted digital pathology.展开更多
This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverag...This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.展开更多
基金Shenzhen Key Fundamental Research Project(No.JCYJ20210324120012035).
文摘Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images.One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images,inheriting the advantages of information-abundant MM polarimetric imaging.In this study,we develop a model using unpaired MM polarimetric images and bright-field images for generating standard hematoxylin and eosin(H&E)stained tissue images.Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images,the proposed Cycle-Consistent Generative Adversarial Networks(CycleGAN)-based model simplifies data acquisition and data preprocessing to a great extent.The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-field images as a viable approach,which provides an intuitive manner for pathologists for future polarization-assisted digital pathology.
基金Funded by 973 Program of Ministry of National Defense of China(Grant No.613237)
文摘This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.
