Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
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
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
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.
