Modulai Blog

Introduction

Synthetic data generation has in recent years emerged as an alternative way to manually gather and annotate data sets. The advantages of a data synthesizer are mainly two-fold. Firstly, it drives the cost of acquiring data to almost zero while eliminating almost all data processing work. Secondly, it has the potential to enable quick prototyping of new machine learning ideas even when data is not available. To put it simply, a well-constructed synthetic data generator produces data in a ready-to-go format for any machine learning case. However, the challenge with synthetic data generators is that they are often domain-specific with a narrow application area and far less mature than methods for network architecture and learning.

In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools has grown and improved significantly over the years. The development has not been in the field of machine learning, though, but rather in game engines such as Unreal Engine, Blender, and Unity. Often produced by professional designers, realistic scenes are produced offering great details as visualized in the video below.

Modulai has deployed models trained on synthetic data in several projects and now seeks to understand the potential of 3D rendered data for solving computer vision tasks. Specifically, in this project, the task is to generate synthetic data images consisting of text using Unreal Engine. The data will be used to train a network with the aim of detecting text in images captured from real environments.

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Modulai Blog

Introduction

Deep learning models are everywhere. They have helped us push the boundaries of what we thought was possible in many different fields, from computer vision to natural language processing. Owing to their complexity, however, deep learning models are in one key sense flawed. They are effectively black boxes: data goes in, prediction comes out – but we can’t really explain how the model arrived at its conclusion. 

Thankfully, an increasingly large group of machine learning researchers are devoting themselves to the field of explainability, or explainable artificial intelligence. They are concerned with peeking inside the black boxes of deep learning models. In this post, we take a look at how explainability techniques can be used to highlight what features of an ECG are most relevant for a model predicting Atrial Fibrillation from sinus ECGs.

Case study

Together with Zenicor Medical Systems AB, Modulai has previously developed a CNN-architecture to detect paroxysmal atrial fibrillation (AF), based on single-lead sinus ECGs. Unless you’re already into medicine, you may be scratching your head at some of what you just read. Let’s break it down quickly.

AF is a type of heart arrhythmia that affects a significant portion of the population. It increases the risk of heart failure and stroke. Given the risks and prevalence, effective AF screening has the potential to save many lives and reduce the burden on healthcare systems worldwide.

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Modulai Blog

Background

Machine learning (ML) in the healthcare sector is an interesting area with a lot of important applications. In this post, we give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes. The primary aim was to predict the root cause of hypoglycemic events (i.e. periods of low blood glucose) using time series data consisting of Continuous Glucose Measurements (CGM).

Furthermore, interpreting how the deep learning model makes predictions based on the CGM time series alone could improve the scientific understanding of CGM classification and help clinicians understand the patients’ glucose measurements in a more refined way. Model interpretation is an active research area within ML and may be applied also to other clinical applications from a broader perspective. 

The team on Modulai’s side consisted of Gustav Eklund and Amund Vedal as machine learning engineers and Puya Sharif and Josef Lindman Hörnlund as project advisors.

This post might be updated in the future with results and performance metrics contingent on the submission and acceptance of the research paper.

Modulai Blog

Introduction

The esports industry has seen tremendous growth lately. Each tournament is streamed live and reaches several million viewers all around the world, increasing the demand for live updates of games, players, e.g. for live betting and more.
Such information can sometimes be accessed directly through the games’ servers and API integrations, but in most cases, it is limited in volume and accessibility.

To improve the experience of watching these tournaments, Abios Gaming provides an API for live information on games, teams, and players. To strengthen Abios Gaming’s offer, and to enable real-time monitoring of esports games, Modulai joined forces with their tech team to build a deep learning object detection solution, to extract information on-the-fly from real-time video streams of gaming tournaments. The system was required to detect both text and icons, and the extracted information was then used to update the status of live matches (e.g. Counter-Strike: Global Offensive (CS:GO), League of Legends, Dota 2, and Fortnite). Moreover, the framework needed to be general enough to be applied to new games easily, in an AWS production environment.

In this post, we will walk through how we addressed the first selected use-case (CS:GO). We will introduce some essential methodological pieces (Such as Scene text detection [1] [2] to detect and recognize text from game frames, and Mask R-CNN [3] to detect icons) as well as synthetic data generation. We then describe the results and end with some conclusions and lessons learned.