Algotec-Philips, part of Philips Radiology Informatic, is a world-renowned pioneer in enterprise software for medical imaging, utilizing cutting-edge technologies in machine learning, computer vision, big data, and natural language processing (NLP). Philips Radiology Informatics provides healthcare facilities with advanced Web-enabled solutions for medical imaging image management, reading, processing, reporting, and distribution. Algotec-Philips’ products are the choice of the world’s top healthcare institutions, including NIH (The US National Institutes of Health), Johns Hopkins Hospital, Cedars Sinai (Beverly Hills), Institute Curie, Sheba Medical Center and over 2,000 institutions around the globe.
Operating in a highly competitive industry where product quality is of utmost importance, the Philips Radiology Informatics team knew that product delivery and quality were critical, and maintaining a competitive edge would continue to be driven by R&D productivity, processes and workﬂow. Efﬁciency in product development is always difﬁcult to measure, but the team knew that they would see results if they could ﬁnd a tool to drive improvements in the areas where most companies, suffered:
It was a long, ambitious list, with hurdles familiar to most development organizations. Philips set out to ﬁnd a tool that would address as many of these challenges as possible.
The Philips team’s search brought them to ClearML, which ticked all the checkboxes. After installation of the experiment management module, the system began tracking and managing data experiments almost immediately, without additional integration efforts by their algorithm team. Immediately, they felt the difference as efﬁciency rose in the various areas they had targeted.
First, ClearML’s experiment manager “automagically” captures metrics and parameters with the addition of a very few lines of code, so the research basically documents itself. “It was impressive to watch even this one feature begin to operate,” says Ohad Silbert, Algorithms Group Leader. “It was like we’d hired a whole team of perfectionist transcriptionists who didn’t need to be managed.”
Next, data transparency changed the dynamic for team leaders who needed to manage the workﬂows and plan ahead: Not only do they now come to meetings better prepared and ready to get down to strategic work, but often update meetings were completely eliminated as the information was easily accessed and reviewed.
The ClearML built-in visualization tools elegantly expand the effectiveness of Philips’ existing Tensorboards to offer new ways to slice and dice data, for both the developers and their managers. Suddenly, this comprehensive amalgamation of parameters, models and underlying metrics paint a very concrete, complete picture, on their own or in comparisons to related experiments. Ohad explains: “The big leap here is that logging of our Tensorboards happens automatically. Moreover, they are automatically associated with the code and hyperparameters that produced them, so developers can compare experiments tying them all up together.”
Finally, ClearML provides an API and UI to manage experiments in a consistent, standardized manner. “We realized that implementing all of our requirements would be too time consuming” says Ohad, “There were simply too many functions we needed to design, build and continuously tweak. ClearML gives us exactly what we’d been envisioning in that system.”
Overall, performance across the algorithm’s team has signiﬁcantly improved. As Evi Kopelowitz, an algorithm developer explained, “ClearML creates an unexpected ‘calming’ feeling because all the data is just … right there. I can instantly ﬁlter, search for and archive results, and organize experiments using the Leaderboard. For instance, there was a day I needed to retrieve critical data from a previous experiment, but I simply didn’t remember its name. But I did remember the results, so I searched for that, and found exactly what I needed. It sounds simple, but it saved me hours of work. And this kind of thing now happens all the time. Those saved hours add up quickly.”
Automatic documentation has also freed up hours per week. Now the team can review past and current experiments, as each is documented cleanly and consistently. Meeting time has been cut down, and the meetings that do occur take less time as they are based on empirical data that’s simpler to collect and understand. With new levels of efﬁciency, the Philips team is now demonstrating measurable boosts in productivity.
Evi explains a typical challenge that’s eliminated by ClearML: “When we run a typical results comparison, we start by comparing all experiments with a speciﬁc hyperparameter or metric. Once we identify a few of the best performing experiments, we dive deeper: We compare metrics and graphs, and ﬁnally, we look at hyperparameters to determine the source of those differences. Often, those hyperparameters still don’t explain the variance in performance, and we’re left with time-consuming code comparison. With Trains, everything is under one roof; I don’t have to go out of the platform, or waste time writing custom one-off code for simply analyzing my experiments. Although we knew we needed this type of organization, the contribution to the efﬁciency of our work exceed our expectations.”
“When it comes to machine learning, I’ll admit it – it’s easy to develop bad habits,” adds Ohad. “But it’s as if ClearML made a list of our inefﬁciencies, and provided the tools within Trains to zap them one by one, helping us invisibly in the background.”
“The very nature of workﬂows and the complex synthesis of code, models, data repositories and infrastructure means that there are way, way too many moving parts to let us work the way we should,” Evi concludes. “Adopting ClearML’s solution undoubtedly propelled us forward in tracking, managing and documenting our research and delivering our algorithms.”