Abstract: Provided is a utidelone liposome composition. The liposome composition mainly contains utidelone, a phospholipid, and optional cholesterol. The liposome composition does not contain an excipient that is prone to causing an allergic reaction of a human body, has a high drug loading capacity and stability, and has a good industrialization potential. Further provided are a method for preparing the utidelone liposome composition and the use thereof.

Abstract: A method and system for sharing a same GPU by a plurality of services, a device and a storage medium are provided.

Abstract: A method, system and apparatus for configuring a computing resource of a service are provided, which relate to the field of resource configuration. The method includes that: an online learning algorithm that is used for recommending, to each service on a platform, a computing resource quota adapted to a service type of the service is preset (S1); when a new service is launched on the platform, a target computing resource quota adapted to the service type of the new service is recommended according to the online learning algorithm (S2); and initial configuration is performed for a computing resource of the new service according to the target computing resource quota (S3). By means of the method, the computing resource quota adapted to the service type of a newly launched service on the platform may be recommended to the newly launched service according to the online learning algorithm, so as to make computing resource configuration of the service more rational.

Abstract: A method and device for performing based learning on a medical image includes reading raw data of a medical image, performing transformation processing on the data by analyzing a data attribute, and integrating the same into a data format capable of being received by a model to be trained; selecting a transformation method by comparing parameters of the model to be trained and a trained model, so as to perform parameter transformation and apply transformation-based learning to training of the model to be trained for the medical image; and upon finishing model training, applying a parameter of a trained model to image category analysis. The invention further includes a device for performing transformation-based learning on a medical image, including: a data processing module; a transformation-based learning module; and an application module.

Abstract: A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

Abstract: A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

Abstract: A method and device for performing based learning on a medical image includes reading raw data of a medical image, performing transformation processing on the data by analyzing a data attribute, and integrating the same into a data format capable of being received by a model to be trained; selecting a transformation method by comparing parameters of the model to be trained and a trained model, so as to perform parameter transformation and apply transformation-based learning to training of the model to be trained for the medical image; and upon finishing model training, applying a parameter of a trained model to image category analysis. The invention further includes a device for performing transformation-based learning on a medical image, including: a data processing module; a transformation-based learning module; and an application module.