Course Intermediate  11308

Course Introduction："Self-study IT Network Linux Load Balancing Video Tutorial" mainly implements Linux load balancing by performing script operations on web, lvs and Linux under nagin.

Course Advanced  17618

Course Introduction："Shangxuetang MySQL Video Tutorial" introduces you to the process from installing to using the MySQL database, and introduces the specific operations of each link in detail.

Course Advanced  11324

Course Introduction："Brothers Band Front-end Example Display Video Tutorial" introduces examples of HTML5 and CSS3 technologies to everyone, so that everyone can become more proficient in using HTML5 and CSS3.

Course Introduction：According to news on June 14, Microsoft researchers recently demonstrated the LLaVA-Med model, which is mainly used for biomedical research and can infer the pathological conditions of patients based on CT and X-ray pictures. It is reported that Microsoft researchers have cooperated with a group of hospitals and obtained a large data set corresponding to biomedical image text to train a multi-modal AI model. This data set includes chest X-ray, MRI, histology, pathology and CT images, etc., with relatively comprehensive coverage. ▲Picture source Microsoft Microsoft uses GPT-4, based on VisionTransformer and Vicuna language model, to train LLaVA-Med on eight Nvidia A100 GPUs, which contains "all pre-analysis information for each image",

2023-06-15 comment 0  1368

Course Introduction：Editor | Cabbage Leaf’s massively pre-trained base model has achieved great success in non-medical fields. However, training these models often requires large, comprehensive datasets, in contrast to the smaller and more specialized datasets common in biomedical imaging. Researchers at the Fraunhofer Institute for Digital Medicine MEVIS in Germany proposed a multi-task learning strategy that separates the number of training tasks from memory requirements. They trained a universal biomedical pre-trained model (UMedPT) on a multi-task database (including tomography, microscopy and X-ray images) and adopted various labeling strategies such as classification, segmentation and

2024-07-22 comment 0  979

Course Introduction：Editor: KX Spatial Transcriptomics and Multi-omics Data Integration Spatial transcriptomics is a major development after single-cell transcriptomics, making the integration of multi-omics data crucial. SpatialGlue: A graph neural network model with dual attention mechanism. Research teams from the Singapore Agency for Science, Technology and Research (A*STAR), BGI and Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine proposed a graph neural network called SpatialGlue. model, which integrates multi-omics data through a dual attention mechanism to reveal the histologically relevant structure of tissue samples in a spatially aware manner. Advantages of SpatialGlue SpatialGlue is able to combine multiple data modalities with their respective spatial contexts. Compared with other methods

2024-07-03 comment 0  551

Course Introduction：Virginia Tech computer science professor Daphne Yao hopes to improve the predictive accuracy of machine learning models in medical applications. Inaccurate predictions can have life-threatening consequences. These prediction errors can lead to miscalculations of a patient's likelihood of dying or surviving their cancer during an emergency room visit. Her findings were recently published in Medical Communications, a journal dedicated to publishing high-quality research, reviews, and papers across all areas of clinical, translational, and public health research. Many clinical data sets are unbalanced because they are dominated by majority population samples, Yao said. In the typical one-size-fits-all machine learning model paradigm, racial and age differences are likely to exist but may be ignored. Yao and her research team worked with

2023-04-13 comment 0  987

Course Introduction：Drug discovery is a complex, multi-step process involving the intersection of many subdisciplines of chemistry and biology. Human medicinal chemists play an important role in this process with their years of accumulated expertise. So, can artificial intelligence (AI) take on the role that medicinal chemists play in drug discovery? The answer may be yes. Recently, a research team from Novartis Institutes for Biomedical Research (NIBR) and Microsoft Research's Science Intelligence Center (AI4Science) jointly proposed a machine learning model that can partially reproduce the collective knowledge accumulated by professional chemists at work. This type of knowledge is often called "chemical intuition." The research team believes that this method can complement molecular modeling to improve future drug development.

2023-11-02 comment 0  1160