The diagnosis of brain cancer relies heavily on medical imaging techniques, with MRI being the most commonly used. It is necessary to perform automatic segmentation of brain tumors on MRI images. This project intends to build an MRI algorithm based on U-Net. The residual network and the module used to enhance the context information are combined, and the void space convolution pooling pyramid is added to the network for processing. The brain glioma MRI image dataset provided by cancer imaging archives was experimentally verified. A multi-scale segmentation method based on a weighted least squares filter was used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of three-dimensional reconstruction is further improved. Experiments show that the local texture features obtained by the proposed algorithm are similar to those obtained by laser scanning. The algorithm is improved by using the U-Net method and an accuracy of 0.9851 is obtained. This approach significantly enhances the precision of image segmentation and boosts the efficiency of image classification.
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Sun Yat-sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；University of Portsmouth（朴茨茅斯大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis
The D-Lab, Department of Precision Medicine（精准医学系D实验室）；GROW—School for Oncology, Maastricht University（马斯特里赫特大学GROW肿瘤学院）；GIGA Cyclotrone Research Center（GIGA回旋加速器研究中心）；University of Liege（列日大学）；Department of Radiology and Nuclear Medicine（放射与核医学系）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition
Hong Kong University of Science and Technology（香港科技大学）；Sun Yat-sen University（中山大学）；Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）
An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies
Germany Cancer Research Center（德国癌症研究中心）；University of Heidelberg（海德堡大学）；University Hospital Frankfurt（法兰克福大学医院）；Charite Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health（柏林健康研究所）
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data
Sano Centre for Computational Medicine（Sano计算医学中心）；AGH University of Krakow（克拉科夫AGH科技大学）；LPI, ETSI Telecomunicación, Universidad de Valladolid（巴利亚多利德大学电信工程学院LPI）
