How is GPU Cloud Critical to AI?
Medical Imaging is a field where the need for technological innovation cannot be stressed more. As we face a shortage of radiologists and consequent rise in turn around time for diagnosis results the challenges appear to be more evident. Cloud computing and AI offer innovative solutions to tackle these deficiencies. The whole field of healthcare can get an uplift by making use of GPU super computing innovations. But with medical imaging, it is not all that simple to go on the cloud.
Medical imaging is the use of several difference technologies and technique to generate images of body parts, tissues, or organs for use in clinical diagnosis, treatment, and disease monitoring. Technology is being transformed at a very faster pace in the healthcare industry. Medical imaging has been positively affected by this technological disruption. This has been enabled by key developments in the artificial intelligence (AI). Artificial intelligence is the use of computer systems to perform tasks that normally require human intelligence. Through deep learning, computers can construct a wide array of algorithms that can to provide robust and powerful GPU computation for data modeling.
AI-based medical imaging relies on a vast supply of medical case data to train its algorithms to find patterns in images and identify specific anatomical markers. Through rigorous analysis of patterns in a given digital image, the imaging algorithms can derive metrics and output that complement the analyses made by the radiologist, which can be useful for quick diagnosis.
AI algorithms has a distributed pattern. Deep neural network and AI in machine learning can be categorized as parallel problems which means parallel super computing solutions like GPUs can speed up 90%. GPUs are best suited for speeding up distributed pattern of AI algorithms where each unit in distributed system works independent of the other units. A neural network will learn several times faster on a GPU than a CPU.
Artificial Intelligence AI and machine learning play a vital role in continuing competitive advantage and delivering fantastic user experience. GPU is very precious as it accelerates the tensor processing necessary for deep learning applications. A GPU has its own memory that keeps the whole graphics image as a matrix. GPU calculates change in the image using tensor math, whenever any change is made to the image like adding color to the pixel, GPU performs this process much faster instead of redrawing the entire screen every time the image changes. These deep learning approaches have shown impressive performances in resembling humans in various fields, including medical imaging.
Cloud giants are not focused on delivering visualizations of AI results
Medical imaging is associated with high degree latency-sensitive applications and global leaders of the cloud are not able to provide this domain-specific essential requirement. Amazon, Google, Microsoft, Oracle and the rest developed their clouds for long term storage of data and are not necessarily good at holding Big Data for instantaneous interactivity. Medical imaging requires high powered processing techniques.
GPU – Taking computing to another level
GPUs or Graphics Processing Units help deliver high-quality medical images. They are highly effective for deep learning. They are 3000x faster than CPU in processing and can run tasks in a parallel processing manner. They bring down the processing time. GPUs were initially meant for 3D visual effects and gaming applications. However, the computational and convenience offered by these units have opened up possibilities for varied domains. GPU cores have an SIMD (single instruction multiple data) architecture that is more advanced that CPUs and can run a number of tasks parallelly at a given time. GPUs offer high bandwidth while hiding their latency under thread parallelism makes GPU a lot faster.
GPU(Graphics Processing Unit) is considered as heart of Deep Learning, a part of Artificial Intelligence. It is a single chip processor used for extensive Graphical and Mathematical computations which frees up CPU cycles for other jobs. 
GPU wins over CPU, powerful desktop GPU beats weak mobile GPU, cloud is for casual users, desktop is for hardcore researchers.
Equipment under test:
CPU 7th gen i7–7500U, 2.7 GHz (from my Ultrabook Samsung NP-900X5N)
GPU NVidia GeForce 940MX, 2GB (also from my Ultrabook Samsung NP-900X5N)
GPU NVidia GeForce 1070, 8GB (ASUS DUAL-GTX1070-O8G) from my desktop
2 x AMD Opteron 6168 1.9 GHz Processor (2×12 cores total) taken from PowerEdge R715 server (yes, I have one installed at home. Not at my home though) 
How to train your neural net faster?
Before the boom of Deep learning, Google had a extremely powerful system to do their processing, which they had specially built for training huge nets. This system was monstrous and was of $5 billion total cost, with multiple clusters of CPUs.
Now researchers at Stanford built the same system in terms of computation to train their deep nets using GPU. And guess what; they reduced the costs to just $33K ! This system was built using GPUs, and it gave the same processing power as Google’s system. Pretty impressive right? 
|Number of cores||1K CPUs = 16K Crores||3GPUs = 18K Crores|
|GPU Vs CPU|
A method of modifying a three dimensional (3D) volume visualization image of an anatomical structure in real time to separate desired portions thereof. The method includes providing a two dimensional (2D) image slice of a 3D volume visualization image of an anatomical structure, identifying portions of the anatomical structure of interest, and providing a prototype image of desired portions of the anatomical structure. The method then includes using an evolver to evolve parameters of an algorithm that employs a transfer function to map optical properties to intensity values coinciding with the portions of the anatomical structure of interest to generate an image that sufficiently matches the prototype image. If the parameters match the prototype image, the method then includes applying the transfer function to additional 2D image slices of the 3D volume visualization image to generate a modified 3D volume visualization image of the anatomical structure. The method includes using a pattern recognizer to assist the evolver, to classify whether a view is normal or abnormal, and to extract the characteristic of an abnormality if and when detected.
The present invention relates to computer processing of three dimensional medical images and, in particular, to a method and system for modifying a three dimensional (3D) volume visualization image of an anatomical structure in real time to delineate desired portions thereof. 
LifeVoxel’s blending of GPU technology with the cloud has brought about something truly unparalleled. GPUs are thus being used to enhance the AI capabilities in medical imaging. We are aiming to reduce the incidence of human error while reading these images.
LifeVoxel.AI is a visualization solution that delivers and stores diagnostic- quality images. LifeVoxel.AI powerful GPU hardware platform and patented proprietary algorithms on the cloud deliver superior image access speed, so you can view professional- quality image from any device at any time. Utilizing multiple patent applications LifeVoxel.AI overcomes the limitations of network bandwidth and latency, delivering a streamlined, intuitive user experience. Lifevoxel.AI addresses these issues through a combination of AI with GPU cluster super computing.
Predictive intelligent streaming overcomes large data access speed and latency over internet. The use of GPU to manipulate GB of patient data remotely without transmitting data to end user. The visualizations can be accesses on any device on-demand and on real-time. Streaming visualizations are done by predicting next frames. Fast FPS from GPU enable discarding incorrectly predicted frames and generating new ones. Predicted frames are buffered to client overcoming the latency. LifeVoxel.AI cloud’s patented cloud for medical imaging contains a GPU that has processing capability of 50 CPU instances.
High powered GPUs also mean that we can address latency issues in a comprehensive manner. Medical images need to be read and transmitted without any delay as they hold a profound significance when it comes to diagnosis and treatment. Latency has always been an issue. While top-rated content delivery sites have a latency period of 0.5 to 3 seconds, LifeVoxel.AI is successfully displaying predicted frames leads to zero-latency as there are no round trip server to use predicted frames. This is made possible through predictive buffering. In effect, we get zero latency through these techniques. In such a way our solutions address the critical aspect of medical image sharing – instant accessibility. GPUs allow the AI to run freely and provide adaptive visualization. Lifevoxel.AI’s use of powerful technology means that images are always of superior clarity.
Deep Learning and other associated AI technologies can offer great potential when coupled with the cloud and GPU. GPU has enabled complex operations in shorter computation time for training deep neural network (DNN). In the field of medical imaging, LifeVoxel.AI has displayed the power of this blend. Only a decade or more before the whole concept of GPU on the cloud would have been unheard of. Such innovative ideas are set to drive the world of medical imaging and healthcare to further progress. LifeVoxel.AI with its critical technologies is all set to play a leading role in this AI-powered world.
Jen-Hsun Huang of NVIDIA says thus, “it takes an enormous amount of innovation in order to put GPUs into the cloud”. Venture Beat said in 2019, “Cloud like Amazon’s AWS… doesn’t make sense for latency-sensitive application… how [it] will get built is an open question”. This is exactly what LifeVoxel.AI has done.
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