Deep-Learning

NVIDIA CLARA DISCOVERY Eco Nvidia’s entry into the medical field. From the cover we can see a ring structure: Natural Language -> Viral Gene Sequence -> Viral Protein Structure -> New Drug Docking with Viral Protein -> Molecular Docking Dynamics Simulation -> Clinical Trial of Drug Candidate Detecting Medical Images of Patient’s Lungs -> Natural Language Introduction At the Nvidia GTC 2020 Fall Conference, Nvidia announced the NVIDIA CLARA DISCOVERY drug development framework, which aims to accelerate the progress of global drug development. With the news of Nvidia’s acquisition of ARM before, I realized that Nvidia has the ambition to rule the edge computing field in the future. Not surprisingly, this GTC conference Nvidia updated many product lines, including large to data center computing module BlueField-2, small to edge field jetson Nano 2G, the most exciting for me is still ** CLARA DISCOVERY**, because my own short-term goal is how to use edge computing in the medical field, such as individuals can calculate the current status of their own diseases through edge computing devices combined with their own diagnostic data obtained from hospital examinations, as well as judgmental detection of Covid-19 CT, ChestXRay. ...

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 ...