Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

Big medical data mainly include electronic health record data, medical image data, gene information data, etc. Among them, medical image data account for the vast majority of medical data at this stage. How to apply big medical data to clinical practice? This is an issue of great concern to medical and computer researchers, and intelligent imaging and deep learning provide a good answer. This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress of big data analysis of medical images and the work of our team in the field of big data analysis of medical imagec, especially the classification and segmentation of medical images.

On 3 January 2019, the Chang'e‐4 (CE‐4) touched down on the Von Karman crater located inside the South Pole‐Aitken Basin, providing for the first time the opportunity for in situ measurements of the lunar regolith at the farside of the Moon. The CE‐4 ground penetrating radar reveals that fine‐grained regolith, coarse impact ejecta, and fractured bedrocks lie beneath the exploration path of the Yutu‐2 rover. The variations of regolith permittivity with depth and the radargrams indicate that the CE‐4 site has a fine‐grained regolith layer thickness of 11.1 m, which is about 1.3–3 times higher than the in situ measurement results at the Apollo and Chang'e‐3 (CE‐3) sites except for Apollo 16, possibly due to a faster weathering rate of ejecta deposits compared with coherent basalt substrates. The penetration depth of CE‐4 is about 2.85 times (in terms of round‐way delay) deeper than CE‐3, probably due to the differences in abundances of ilmenite and rocks in the regolith.