Deep-Learning

Using graph neural networks in high-energy particle physics Reference image and article address: [The next big thing: the use of graph neural networks to discover particles](https://news.fnal.gov/2020/09/the-next-big-thing- the-use-of-graph-neural-networks-to-discover-particles/) Reflections The article describes the development of a set of graph neural network models by the Institute of High Energy Physics Fermilab in order to filter valuable images from the huge amount of particle collision images, which are deployed in the LHC lab to directly process the data generated during particle collisions. ...

Research References What are lung opacities? This post is a continuation of the Covid-19 Xray lung detection, because when processing the pre-sample data, we need to be able to know more clearly how the medical field processes and analyzes these x-ray maps so that we can better design our own deep neural network architecture. Let’s first look at a lung x-ray of our normal person and determine if there is a lesion in the lung by the black, white and gray in the image. ...

A Review of Automated Diagnosis of COVID-19 Based on Scanning Images In the last week, I have been optimizing the Covid-Net I developed with my thesis and other research materials to diagnose the deep learning model of Covid19, and the accuracy I got from training on 20,000+ xRay datasets is less than 90%. When the model became more and more complex, the video memory was obviously not enough, so I sold the DJI Inspire 2 and used the money I earned to buy a TITAN RTX 24G, and then continued to optimize the model and increase training. of the batch_size. ...

The animation program code is as follows: lung-3D.nb lung3d = AnatomyPlot3D[lung anatomical structure, PlotTheme->"XRay"] gif = {}; Do[ image = ImageResize[ Show[lung3d, ViewPoint -> {3 Cos[x], 3 Sin[x], 0}, ViewAngle -> 20 Degree], {256, 256}]; gif = Append[gif, image], {x, 0, 2 Pi, 0.1}] Export["lung-3D.gif", gif, "AnimationRepetitions" -> Infinity ] image = ImageResize[ Show[lung3d, ViewPoint -> {3 Cos[Pi/2], 3 Sin[Pi/2], 0}, ViewAngle -> 20 Degree], {512, 512}] Training datasets covid-chestxray-dataset ...

Horovod I have written an article about how to build a distributed training cluster on Raspberry Pie 4 using the distributed training system that comes with TF2.0. However, there is a drawback: we need to start the training program at each node, and the distributed training will only work after all the nodes are started. MPI is mainly used in the field of supercomputing. Building MPI cluster on Raspberry, firstly, it can be used to learn distributed computing on supercomputing, and secondly, it can observe the performance of TF> distributed training on ARMv8 in practice. ...