Abstract: A metal powder preparation system and method are provided. The metal powder preparation system includes a medium frequency smelting furnace, a homogeneous insulated quantitative pouring furnace, a precision-controlled liquid level temperature pouring ladle, and a plurality of groups of atomization mechanisms connected in sequence. The present application improves the preparation quality of the metal powder, so that the parameters such as the powder particle size, sphericity, fluidity, oxygen content, component distribution, and particle size distribution of the metal powder can all meet the requirements of high-quality metal additive manufacturing, achieving efficient and continuous preparation of the metal powder at the same time.

Abstract: A tumor diagnosis system and a construction method thereof, a terminal device and a storage medium are provided, wherein a radiomics signature is obtained by a radiomics signature-generation module in the tumor diagnosis system according to target radiomics information and clinical data information, based on a pre-trained radiomics signature model; a first classification comprehensive diagnosis result is obtained by a benign and malignant comprehensive classification module according to a ambient situation of a lesion outside an intestinal wall, the clinical data information and a first radiomics signature, based on a pre-trained first classification comprehensive diagnosis model; a second classification comprehensive diagnosis result is generated through a malignant focus sub-classification module based on a pre-trained second classification comprehensive diagnosis model, if the first classification comprehensive diagnosis result includes high-risk malignant information; and a final diagnosis result is obtai

Abstract: A system for predicting microsatellite instability and a construction method thereof, a terminal device and a medium are provided. Target image information, pathological specimen information and clinical data information of a user to be predicted are acquired by an acquisition module in the system for predicting microsatellite instability; a radiomics signature is generated according to the target image information, and a pathomics signature is generated according to the pathological specimen information, by a signature generation module, based on a pre-trained MSI-H/dMMR multi-omics signature model; and MSI-H/dMMR prediction results are generated according to the radiomics signature, the pathomics signature and the clinical data information, by a prediction generation module, based on a pre-trained MSI-H/dMMR prediction model.

Abstract: A system for predicting microsatellite instability and a construction method thereof, a terminal device and a medium are provided. Target image information, pathological specimen information and clinical data information of a user to be predicted are acquired by an acquisition module in the system for predicting microsatellite instability; a radiomics signature is generated according to the target image information, and a pathomics signature is generated according to the pathological specimen information, by a signature generation module, based on a pre-trained MSI-H/dMMR multi-omics signature model; and MSI-H/dMMR prediction results are generated according to the radiomics signature, the pathomics signature and the clinical data information, by a prediction generation module, based on a pre-trained MSI-H/dMMR prediction model.

Abstract: A tumor diagnosis system and a construction method thereof, a terminal device and a storage medium are provided, wherein a radiomics signature is obtained by a radiomics signature-generation module in the tumor diagnosis system according to target radiomics information and clinical data information, based on a pre-trained radiomics signature model; a first classification comprehensive diagnosis result is obtained by a benign and malignant comprehensive classification module according to a ambient situation of a lesion outside an intestinal wall, the clinical data information and a first radiomics signature, based on a pre-trained first classification comprehensive diagnosis model; a second classification comprehensive diagnosis result is generated through a malignant focus sub-classification module based on a pre-trained second classification comprehensive diagnosis model, if the first classification comprehensive diagnosis result includes high-risk malignant information; and a final diagnosis result is obtai