The China Seismo-Electromagnetic Satellite, launched into orbit from Jiuquan Satellite Launch Centre on February 2 nd, 2018, is China's first space satellite dedicated to geophysical exporation. The satellite carr...The China Seismo-Electromagnetic Satellite, launched into orbit from Jiuquan Satellite Launch Centre on February 2 nd, 2018, is China's first space satellite dedicated to geophysical exporation. The satellite carries eight scientific payloads including high-precision magnetometers to detect electromagnetic changes in space, in particular changes associated with global earthquake disasters. In order to encourage and facilitate use by geophysical scientists of data from the satellite's payloads, this paper introduces the application systems developed for the China Seismo-Electromagnetic Satellite by the Institute of Crustal Dynamics, China Earthquake Administration;these include platform construction, data classification, data storage, data format, and data access and acquisition.展开更多
Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hil...Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hill and gully area of northern Shaanxi Province as a test area, a research was conducted to extract sloping field and other land use categories by applying an integrated classification. Based on an integration of supervised classification aad unsupervised classification, sampling method is remarkably unproved. The results show that the classification accuracy is satisfactory by the method and is of critical significance in obtaining up-to-date information of the sloping field, which should be helpful in the state key project of converting farmland to forest and grassland on slope land in this area. This research sought to improve the application accuracy of image classification in complex terrain areas.展开更多
Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory resear...Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research.In the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was proposed.First,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data.Second,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps.Then,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory point.To validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories.On the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.展开更多
Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,...Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.展开更多
An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin(H&E) histology, such as frozen section, are time-,resource-, and l...An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin(H&E) histology, such as frozen section, are time-,resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography(D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group(n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma(IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ(DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests(n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%;only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow(approximately 3 min)was significantly shorter than that required for traditional procedures(approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.展开更多
基金supported by the Civil Space Research project (ZH1 data validation: Ionospheric observatory theory)NFSC grant 41574139 and 41874174
文摘The China Seismo-Electromagnetic Satellite, launched into orbit from Jiuquan Satellite Launch Centre on February 2 nd, 2018, is China's first space satellite dedicated to geophysical exporation. The satellite carries eight scientific payloads including high-precision magnetometers to detect electromagnetic changes in space, in particular changes associated with global earthquake disasters. In order to encourage and facilitate use by geophysical scientists of data from the satellite's payloads, this paper introduces the application systems developed for the China Seismo-Electromagnetic Satellite by the Institute of Crustal Dynamics, China Earthquake Administration;these include platform construction, data classification, data storage, data format, and data access and acquisition.
基金National Nature Science Foundation of China,No.40271089High-visiting scholar fund of The Key Laboratory of LIESMARS
文摘Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hill and gully area of northern Shaanxi Province as a test area, a research was conducted to extract sloping field and other land use categories by applying an integrated classification. Based on an integration of supervised classification aad unsupervised classification, sampling method is remarkably unproved. The results show that the classification accuracy is satisfactory by the method and is of critical significance in obtaining up-to-date information of the sloping field, which should be helpful in the state key project of converting farmland to forest and grassland on slope land in this area. This research sought to improve the application accuracy of image classification in complex terrain areas.
基金supported by the National Natural Science Foundation of China(Grant No.32301691)the National Key R&D Program of China and Shandong Province,China(Grant No.2021YFB3901300)the National Precision Agriculture Application Project(Grant/Contract number:JZNYYY001).
文摘Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research.In the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was proposed.First,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data.Second,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps.Then,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory point.To validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories.On the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.
基金supported by the National Natural Science Foundation of China(Grant Nos.62175156,81827807,81770940)Science and Technology Commission of Shanghai Municipality(22S31903000,16DZ0501100)Collaborative Innovation Project of Shanghai Institute of Technology(XTCX2022-27).
文摘Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.
基金supported by the Capital’s Funds for Health Improvement and Research (CHF 2020-2Z-40812)Beijing Natural Science Foundation (7242281)+4 种基金Beijing Municipal Science and Technology Project (Z201100005520081)the National Key Research and Development Program of China (2016YFC0901300)the National Natural Science Foundation of China (62076015)Macao Science and Technology Development Fund,Macao,China (0070/2020/A2,0003/2021/AKP)Macao Young Scholars Program (AM2023024)。
文摘An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin(H&E) histology, such as frozen section, are time-,resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography(D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group(n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma(IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ(DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests(n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%;only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow(approximately 3 min)was significantly shorter than that required for traditional procedures(approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.
