Hey Folks! Here we come with our fourth Heartbeat: we are glad to present a super nice use case for Hangar, and yet very contemporary considering the situation we are facing right now with the SARS-CoV-2 global pandemic. Moreover, we will be talking about RedisConf 2020, the international Redis conference which is happening this week.
Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.
A collaborative annotation tool for covid19 datasets
Today we are proud to present a practical use case for Hangar we are working on. We are building a collaborative image annotation tool on top of the secure foundations of Hangar, with the hope it could serve the community in these times of emergency.
Using this system, you get annotations versioning and the choice of the best backend storage for your data for free. Moreover, this enables collaborative dataset curation amongst different collaborators, without the hassle of maintaining a collection of (maybe) CSV files with names, timestamps, or even worse … formatted Excel files (yes - there are plenty of people still doing that).
The annotation interface is built on top LOST and it is based on a coarse point counting grid. Stereology has been proved to be an effective approach in reducing the cost of annotation, yet enabling the training of a segmentation model and achieve a satisfying performance.
You could either decide to distribute the annotation workload across annotators, assigning to each of them a different Hangar branch. Each person sees only a subset of the data and can carry out the task individually. When the annotators are done, you can simply merge all the branches and get the annotations for the whole dataset.
Otherwise, you could still assign a different Hangar branch for each person involved with the annotation process, but instead, let them see and annotate the whole dataset. This way you can get a bunch of different annotations for the same image, so you can stop relying on the eyes of a single radiologist. This would also help to overcome the bias coming from having one image seen by only one radiologist and therefore limiting possible batch effects.
If you want to have a closer look at the code, please visit https://github.com/hhsecond/coviddatastore.
RedisConf 2020 Takeaway, May 12-13
Like many other technical conferences in the world, due to the outbreak of COVID-19, RedisConf 2020 became a virtual event. The good news is that it became a free event, so every one of you can actually participate from their own couch, enjoying a live keynote, 50+ breakout sessions, a hackathon, 1:1 office hours with Redis experts, group chats, games, and more. Just tune in on May 12-13!
👉 Registration and more info at the official website. 👈
Our CEO Luca Antiga has also been invited to be a speaker for the conference! Make sure to follow his Breakout Session on RedisAI. You will discover the latest new features coming with the new release of RedisAI 1.0, including auto-batching, DAG commands, MLFlow integration and revamped docs. You’ll also get a glimpse of what’s baking for the next releases. In case you want to find out more about it, please check out our last heartbeat, which was entirely dedicated to RedisAI!
Meet the people: Luca Antiga
Luca (lantiga on Twitter and GitHub) is a co-founder and CEO at Tensorwerk.
As a kid, he started coding on his Sinclair ZX Spectrum 48k in the mid-’80s, but he didn’t do much with it until much later in life (he still thinks that having BASIC instructions stamped on the keyboard was a great way to get a kid’s attention). A bioengineer in training, he went on as a researcher in medical image analysis and cardiovascular biomechanics in the 2000’s. He picked up C++ and Python, and after noodling with connections between vascular morphology, computational geometry and fluid dynamics, he released the Vascular Modeling Toolkit in 2004, an open-source project that is still used to date in bioengineering departments. He later contributed to the Insight Toolkit and 3DSlicer, and authored scientific papers on these subjects.
In 2009 he left research to co-found Orobix, a company based in Bergamo (Italy) initially focused on medical image analysis, and that around 2014 became an AI engineering company operating in different sectors, such as healthcare, manufacturing, gaming, astrophysics. In 2017 Luca started contributing to PyTorch and was a core contributor for a couple of years. In the meantime he started co-authoring Deep Learning with PyTorch for Manning. As Orobix was developing, ideas for new tools filling the gaps in the AI tooling landscape came up. Those ideas converged in an opportunity of a new initiative stemming from the experience at Orobix, but focused on developing core tools for Software 2.0. This is how Tensorwerk came to be. ✨
At Tensorwerk he’s busy with directions and design, and he’s directly involved in the development of RedisAI. Along with family and work, Luca has a passion for listening to jazz and its surroundings. He spends a few hours a week trying to make some sense out of the sounds coming out of his guitar.
If you’d like to have a peek into our vision and our upcoming developments, please send us a note at firstname.lastname@example.org. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.
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