DataBloom

visible = Input(shape=100) Hidden1 = Dense(32, activation = 'relu')(visible) Hidden2 = Dense(64, activation = 'relu')(Hidden1) Hidden3 = Dense(128, activation = 'relu')(Hidden2) Output = Dense(1, activation = 'softplus')(Hidden3) model = Model(inputs=visible, outputs=Output) model.compile(loss='huber',optimizer='adam', metrics=['accuracy']) batch_size = 112 epochs = 200 model.fit(x=x_train,y=y_train, batch_size=batch_size,epochs=epochs) test_loss, test_acc = model.evaluate(x_test, y_test) >Test Loss: 0.06204257160425186, Test Accuracy: 0.8361972570419312 

# image size is 480*640 height = 480 length = 640 X_Try = [] for a in range(0, height, 10): for b in range(0, length, 10): img = image.crop((a, b, a+10, b+10)) img = np.array(img)/255.0 #to normalize my values img = img.flatten() #to make the matrix into a single array X_Try.append(img) print(len(X_Try)) >3072 print(len(X_Try[0])) >100 

y_pred=[] for f in range(0,len(X_Try)): y_p = model.predict(X_Try[f]) y_pred.append(y_p) 

y_pred = model.predict(X_Try[0]) 

y_pred = model.predict(x_test) 

y_pred = model.predict(X_Try) 

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Deep learning models have been successfully used in medical image analysis problems but they require a large, curated amount of labeled images to obtain good performance. Creating such annotations are tedious, time-consuming and typically require clinical expertise. To address this gap, Project MONAI has released MONAI Label v0.1 – an intelligent open source image labeling … Continued

Deep learning models have been successfully used in medical image analysis problems but they require a large, curated amount of labeled images to obtain good performance. Creating such annotations are tedious, time-consuming and typically require clinical expertise.

To address this gap, Project MONAI has released MONAI Label v0.1 – an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets easily and quickly, and build AI models in a standardized MONAI paradigm.

MONAI Label enables the adaptation of AI models to the clinical task at hand by continuously learning from the user’s interactions and new labels. It powers an AI Assisted annotation experience, allowing researchers and developers to make continuous improvements to their applications with iterative feedback from clinicians who are typically the end users of the medical imaging AI models.

At the Children’s Hospital of Philadelphia (CHOP), Dr. Matthew Jolley explains how they are innovating and driving clinical impact with machine learning algorithms.

“Children with congenital heart disease demonstrate a broad range of anatomy, and there are few readily available tools to facilitate image-based structural phenotyping and patient specific planning of complex cardiac interventions. However, currently 3D image-based heart model creation is slow, even in the hands of experienced modelers.  As such, we have been working to develop machine learning algorithms to create models of heart valves in children with congenital heart disease, such as the tricuspid valve in hypoplastic left heart syndrome. Ongoing development of automation based on machine learning will allow rapid modeling and precise quantification of how a dysfunctional valve differs from normal valves across multiple parameters.  That “structural valve profile” for an individual can then be contextualized within the spectrum of anatomy and function we see in the population, which may eventually inform improved medical decision making and interventions for children.”

With MONAI Label we envision creating a community of researchers and clinicians like Dr. Jolley and his team who can build upon a well maintained software foundation that will accelerate collaboration through continuous learning. The MONAI Label team and CHOP collaborated through a Slicer week project, and successfully developed a MONAI Label application for leaflet segmentation of heart valves in 3D echocardiographic (3DE) images. The team is now working to deploy this model as a MONAI Label application on a public facing server at CHOP where clinicians can directly interface with the model and trigger a training loop for adaptation – learn more.

It is incredibly important for an open source initiative like Project MONAI to have clinicians in the loop as we converge to develop a common set of best practices for AI lifecycle management in healthcare imaging. To quote Dr. Jolley:

“Open-source frameworks like Project MONAI provide a standardized, transparent, and reproducible template for the creation of, and deployment of medical imaged-focused machine learning models, potentiating efforts such as ours. They allow us to focus on investigating novel algorithms and their application, rather than developing and maintaining software infrastructure.  This in turn has accelerated research progress which we are actively translating into tools of practical relevance to the pediatric community we serve.”

WHAT IS INCLUDED IN MONAI LABEL V0.1

MONAI Label is an open-source server-client system that is easy to set up and can run locally on a machine with one or two GPUs. The initial release does not yet support multiple user sessions, therefore both server and client operate on the same machine.

MONAI Label delivers on MONAI’s core promise of being modular, Pythonic, extensible, easy to debug, user friendly, and portable.

MONAI v0.1 includes:

• MONAI Label server: REST API server that facilitates communication with the viewer clients i.e. (Slicer, OHIF, etc).
• MONAI Label sample applications that can be adapted to a given clinical task for MR/CT modality including DeepGrow and DeepEdit which are developed by MONAI researchers.
• With v0.1, we have released a 3DSlicer plugin for MONAI Label to  kick start users in the MONAI Label experience

Future releases of NVIDIA Clara AIAA will also leverage the MONAI Label framework. We continue to bring together development efforts for NVIDIA Clara medical imaging tools and MONAI to deliver domain-optimized, robust software tools for researchers and developers in healthcare imaging.

With contributions from an engaged community, MONAI Label aims to reduce the cost of labeling and maximize the collaboration between researchers & clinicians. Get started today with sample applications available on the MONAI Label GitHub and follow along with our step-by-step getting started guide available in the MONAI Label Documentation.

New research out of the University of California, San Francisco has given a paralyzed man the ability to communicate by translating his brain signals into computer generated writing. The study, published in The New England Journal of Medicine, marks a significant milestone toward restoring communication for people who have lost the ability to speak.  “To … Continued

New research out of the University of California, San Francisco has given a paralyzed man the ability to communicate by translating his brain signals into computer generated writing. The study, published in The New England Journal of Medicine, marks a significant milestone toward restoring communication for people who have lost the ability to speak. 

“To our knowledge, this is the first successful demonstration of direct decoding of full words from the brain activity of someone who is paralyzed and cannot speak,” senior author and the Joan and Sanford Weill Chair of Neurological Surgery at UCSF, Edward Chang said in a press release. “It shows strong promise to restore communication by tapping into the brain’s natural speech machinery.” 

Some with speech limitations use assistive devices–such as touchscreens, keyboards, or speech-generating computers to communicate. However, every year thousands lose their speech ability from paralysis or brain damage, leaving them unable to use assistive technologies. 

The participant lost his ability to speak in 2003, paralyzed by a brain stroke following a car accident. The researchers were not sure if his brain retained neural activity linked to speech. To track his brain signals, a neuroprosthetic device consisting of electrodes was positioned on the left side of the brain, across several regions known for speech processing. 

Over about four months the team embarked on 50 training sessions, where the participant was prompted to say individual words, form sentences, or respond to questions on a display screen. While responding to the prompts, the electrode device captured neural activity and transmitted the information to a computer with custom software. 

“Our models needed to learn the mapping between complex brain activity patterns and intended speech. That poses a major challenge when the participant can’t speak,” David Moses, a postdoctoral engineer in the Chang lab and one of the lead authors of the study, said in a press release.

To decode the responses from his brain activity, the team created speech-detection and word classification models. Using the cuDNN-accelerated TensorFlow framework and 32 NVIDIA V100 Tensor Core GPUs the researchers trained, fine-tuned, and evaluated the models.

“Utilizing neural networks was essential to getting the classification and detection performance we did, and our final product was the result of lots of experimentation,’ said study co-lead Sean Metzger. “Because our dataset was constantly evolving and growing, being able to adapt the models we were using was critical. The GPUs helped us make changes, monitor progress, and understand our dataset.”

With up to 93% accuracy, and a median rate of 75%, the model decoded the participants word’s at a rate of up to 18 per minute.  

“We want to get to 1,000 words, and eventually all words. This is just the starting point,” Chang said. 

The study builds off previous work by Chang and his colleagues, which developed a deep learning method for decoding and converting brain signals. Unlike the current work, participants in the previous study were able to speak. 

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Read the full article in The New England Journal of Medicine >>>

A new session from GTC shares how to use synthetic data and Fleet Command to deploy highly accurate and scalable models.

The retail supply chain is complex and includes everything from creating a product, distributing it, putting it on shelves in stores, to getting it into customer hands. Retailers and Consumer Packaged Goods (CPG) companies must look at the entire supply chain for critical gaps and problems that can be solved with technology and automation. Computer vision has been implemented by many of these companies for years, with cameras distributed in their stores, warehouses, and on assembly lines. This is where edge computing comes in, AI applications can be run in remote locations that allow companies to turn these cameras from sources of information to sources of intelligence. With AI, these cameras can turn from sources of information to sources of intelligence. Whether providing in-store analytics to help evaluate traffic patterns and optimize product placement, to improving packaging detection and analysis, and overall health and safety within warehouses.

The challenge with computer vision applications in the retail space is the heavy data requirement that is needed to ensure AI models are accurate and safe. Once trained, these models then need to be deployed to many locations at the edge, often without the IT resources onsite. Kinetic Vision has partnered with NVIDIA to develop a new solution to this problem that allows retailers and CPG companies to generate accurate models, and scale them out at the edge.

Solving the challenge of data is key to enabling the training of AI models using NVIDIA tools like the DeepStream SDK and Transfer Learning Toolkit (TLT). With a Synthetic Data Generator, Kinetic Vision not only produces data volume, but also with the required variances to ensure the model will perform in any environment. Numerous angles, lighting, backgrounds, and product types can be generated quickly and easily using different methods including GANs, simulated sensor data (LIDAR, RADAR, IMU), photorealistic 3D environment, synthetic x-rays, and physics simulations. 

The synthetic data is then used to train a model that can be tested in a digital twin, a virtual representation of the warehouse, supply line, store, or whatever environment the model will be deployed. Using the synthetic data and the digital twin, Kinetic Vision can train, simulate, and re-train the model to achieve the required level of accuracy. 

Once the AI model has achieved the desired level of performance, it must be tested in the real world. This is where NVIDIA Fleet Command comes in. Fleet Command is a hybrid-cloud platform for deploying and managing AI models at the edge. The pre-trained model is simply loaded into the NGC catalog and then deployed on the edge system using the Fleet Command UI in just a few clicks. Once deployed at the edge, the model can continue to be optimized with real world data sent back from the store or warehouse. These updates are once again easily deployed and managed using Fleet Command. 

The advantages of this new approach to creating retail computer vision applications include both ROI and technological benefits. The cost of developing an AI model with a digital twin is easily 10 percent the time and cost required to do the same thing in a physical environment. With the digital twin, testing can be done without physical infrastructure or requiring production interruptions. Additionally, new products and product variations can be easily accommodated without requiring inventory photography that must be manually annotated. Finally, the digital twin results in a generalized and scalable model that still provides the accuracy required for production deployment.

To learn more about how to use synthetic data and Fleet Command to deploy highly accurate and scalable models, check out the GTC session “Novel Approach to Deploy Highly Accurate AI Retail Computer Vision Applications at the Edge“.

‘Meet the Researcher’ is a series in which we spotlight different researchers in academia who use NVIDIA technologies to accelerate their work.  This month we spotlight Dr. Emanuel Gull, Associate Professor of Physics at University of Michigan, whose research focuses on the development of theoretical and computational methods for strongly correlated quantum systems.  Gull is … Continued

‘Meet the Researcher’ is a series in which we spotlight different researchers in academia who use NVIDIA technologies to accelerate their work. 

This month we spotlight Dr. Emanuel Gull, Associate Professor of Physics at University of Michigan, whose research focuses on the development of theoretical and computational methods for strongly correlated quantum systems. 

Gull is the recipient of a Sloan Research Fellow, Ralph E. Powe Junior Faculty Enhancement Award, DOE Early Career Research Award, SCES early career Nevill F. Mott Prize, and APS Outstanding Referee Program.

What are your research areas?

The physics of materials in which many quantum particles strongly interact with each other. These are the systems out of which we build our newest generation of magnets, superconductors, solar cells, and systems for standard approximative methods.

When did you know that you wanted to be a researcher and pursue this field?

I was always open to having a career in the software/computing side of industry/finance — but, when I had to decide whether to go for a postdoc, the financial crisis hit. Instead, I did a postdoc in the U.S. and managed to get hired into an academic position afterwards.

What motivated you to pursue your recent research area of focus?

‘Quantum’ theory is the reason why many of our recent technological breakthroughs work. After all, NVIDIA chips are just an application of quantum theory. However, taking just theory and predicting and improving material properties without further input is incredibly difficult, even though we believe we understand the theory very well. I have always been fascinated by the challenge of combining computers and theoretical methods to bring calculations closer to reality. This started with an internship I did at a high performance computer center back when I was a high school student.

What problems or challenges does your research address? 

While we know the equations that govern the physics of systems with many interacting quantum particles well, they are impossibly difficult to solve. This is why we need to find approximations that are both numerically tractable and accurate. My research spans the entire gamut from theoretical derivations, to implementation of new algorithms, to HPC, to comparisons with experiments. All of my research aims to make quantum theories more predictive and more accurate.

What challenges did you face during the research process, and how did you overcome them?

Time management is probably the most crucial. It’s easy to have many ideas, but testing them, improving upon them, and revising them takes time. In research, you’re constantly juggling finding resources, training people, having and revising ideas, publishing, going to conferences, etc. Finding quiet intervals to work deeply on a problem is essential, but difficult. I don’t believe I’ve overcome that limitation.

What is the impact of your work on the field/community/world?

Stronger magnets, higher temperature superconductors, and better materials for sensors and chips.

How have you used NVIDIA technology either in your current or previous research?

Yes! In fact, our home-written ab-initio simulation toolkit uses NVIDIA codes to simulate the physics of real materials and their excitations. Most of our calculations would be either impossible or borderline without the NVIDIA fast and double-precision arithmetics on the V100 and A100. Our codes run at just about 50% of theoretical peak flop, and are parallelized with streams within each GPU and with MPI between different GPUs and nodes.

What research breakthroughs or interesting results can you share?

We did, and we’re just now writing a paper on a new high-temperature superconductor. 

What’s next for your research? 

We’re currently doing a big push for driving systems out of equilibrium. We’re exciting them with a laser, ‘quenching’ them with a short current pulse, or probing them in other nonequilibrium conditions. The nonequilibrium physics of quantum materials is very different from the equilibrium conditions, and many exciting new phenomena appear. Besides, most sensors work out of equilibrium. How to generalize our computational toolkit to these situations is currently an open question that we’re working on.

Any advice for new researchers, especially to those who are inspired and motivated by your work?

Ask the big questions. Why is this interesting? Why will it work or how will it not work? What have we learned if it does work? But, don’t lose sight of the small details. Pounce at the details that don’t quite make sense, that’s where there’s something that needs to be understood. When something turns into a dead end, learn to let it go (even if you’ve invested a lot of resources into it). 

Also, know the established ways of thinking about a problem, but question them always. Know the limitations of your tools and theories, and invest in your toolkit. New tools (computer codes, theoretical methods, experimental setups) lead to new discoveries, so make sure you have the best ones available for your application.

A pair of new demos running GeForce RTX technologies on the Arm platform unveiled by NVIDIA today show how advanced graphics can be extended to a broader, more power-efficient set of devices. The two demos, shown at this week’s Game Developers Conference, included Wolfenstein: Youngblood from Bethesda Softworks and MachineGames, as well as The Bistro Read article >

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