Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires high operator skill, and its interpretation and analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers and make imaging less dependent on operator expertise, however most ultrasound systems only have 2D imaging capabilities. We propose both a simple alteration to the Pix2Vox++ networks for a sizeable reduction in memory usage and computational complexity, and a pipeline to perform reconstruction of 3D anatomy from 2D standard cardiac views, effectively enabling 3D anatomical reconstruction from limited 2D data. We evaluate our pipeline using synthetically generated data achieving accurate 3D whole-heart reconstructions (peak intersection over union score > 0.88) from just two standard anatomical 2D views of the heart. We also show preliminary results using real echo images.

Maybe we are interested in knowing what's likely to happen in each game they play. In this case, knowing the total number of home runs hit over the course of the season isn't going to be quite as helpful – to make an accurate forecast about the next game, we need to have game-level data. Is the game being played at home or away? The answers to these questions are all crucial to generating an accurate prediction of the Giants' next game. This type of forecast is called a'bottom-up' or'rollup'-based forecast because predictions are made for each game based on the Giants' probability of winning each matchup.

The CRM is no longer a data repository and a basic workflow engine that creates static reports. Thanks to AI and ML, predictive and prescriptive insights can be embedded into CRM. This is known as the "intelligent experience." The intelligent experience is a natural fit for sales forecasting. We all know data is incredibly powerful, but often its potential goes untapped in businesses.

Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. One of the most sought-after problems in echo is the segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an echocardiogram generation approach using generative adversarial networks with a conditional patch-based discriminator. In this work, we validate the feasibility of GAN-enhanced echo generation with different conditions (segmentation masks), namely, the left ventricle, ventricular myocardium, and atrium. Results show that the proposed adversarial algorithm can generate high-quality echo frames whose cardiac structures match the given segmentation masks. This method is expected to facilitate the training of other machine learning models in a semi-supervised fashion as suggested in similar researches.

In this special guest feature, Chris Heineken, CEO and Co-founder of Atrium, suggests that as Machine Learning (ML) is growing in the IT and cloud space, understanding how to best utilize its capabilities will change the approach to implementing new IT investments. As CEO of Atrium, Chris leads a world-class team in empowering companies to embrace the next generation of tech through the power of AI. Prior to founding Atrium, Chris was the COO at Appirio where he was responsible for leading the Company's global consulting, sales, and operations teams. Chris started his career with Accenture and later founded Bay Street Solutions, a CRM/Siebel consulting firm, acquired by Perficient. He earned his undergraduate degree from UC Davis and MBA from UC Berkeley.

Machine learning has been around for quite some time and we see or use it knowingly or unknowingly in our daily lives. The best example comes the moment when we open our emails – THE SPAM FILTER! It saves you a lot of time by automatically keeping the most important emails in your inbox and moving the suspicious ones to your spam folder. Let's look at how machine learning is defined, how it helps our everyday processes, and the different types of machine learning. Machine learning is a method of data analysis that automates analytical model building.

In preoperative planning of left atrial appendage closure (LAAC) with CT angiography, the assessment of the appendage orifice plays a crucial role in choosing an appropriate LAAC device size and a proper C-arm angulation. However, accurate orifice detection is laborious because of the high anatomic variation of the appendage, as well as the unclear orifice position and orientation in the available views. We propose an automatic orifice detection approach performing a search on the principal medial axis of the appendage, where we present an efficient iterative algorithm to grow the axis from the appendage to the left atrium. We propose to use the axis-to-surface distance of the appendage for efficient and effective detection. To localize the necessary initial seed for growing the medial axis, we train an artificial localization agent using an actor-critic reinforcement learning approach, defining the localization as a sequential decision process. The entire detection process takes only about 8 seconds, and the variance of the detected orifice with respect to annotations from two experts is calculated to be significantly small and less than the inter-observer variance. The proposed orifice search on the medial axis of the appendage comparing only its distance from the surface provides a simple, yet robust solution for orifice detection. While being the first fully automatic approach and providing a detection error below the inter-observer difference, our method improved the detection efficiency by eighteen times compared to the existing solution, therefore, can be potentially useful for physicians.

Our learnings this year bring us to the same conclusion as McKinsey. There is significant money on the table for companies in almost any setting. In retrospective, the briefings, discovery and strategy workshops we facilitated consistently generated simple investment opportunities in Machine Learning that represented high impact outcomes for our clients. These investment options centered around using math at scale to help enterprises become more productive across common challenges like lead scoring, win rate conversion, forecasting, customer attrition and cross selling. From a career growth perspective, expanding individual skills around'Math at Scale' as a discipline represents unique career opportunities for those looking to improve their internal influence and span of control.

The rise of the robots has plagued the imagination of science-fiction since its inception. Whether it involved Asimov's three rules of robotics or the time travelling robotic psychopaths in the Terminator franchise, the world has worried about the role of robotics. The world has prepared itself for the robotic invasion with many low skilled jobs in retail, customer services and industrial industries like factories all replacing humans with robotic counterparts to some extent; many of which improve efficiency, productivity and costs. Shockingly, San Francisco-based company, Atrium, believe that robots have the potential to supersede and complete the job of some high-paid legal service workers. As young as 14 months, Atrium, along with the $65 million investments from adventure capitalists, are set to revolutionise the legal sector by using artificial intelligence to work alongside legal service professionals, and in some cases, replace them.

Let the computers do the legal busy work so attorneys can focus on complex problem solving for their clients. That's the lucrative idea behind Atrium LTS, Twitch co-founder Justin Kan's machine learning startup that digitizes legal documents and builds applications on top to speed up fundraising, commercial contracts, equity distribution and employment issues. For example, one of its apps automatically turns startup funding documents into Excel cap tables. Automating expensive legal labor has led to a rapid rise to 110 employees and 250 clients for Atrium, including startups like Bird and MessageBird. Atrium only came of stealth a year ago with a $10.5 million party round before going into Y Combinator last winter.