ZEISS Innovation Hub @ KIT off to a successful start
Projects show innovative strength gleaned through research partnerships
- New thesis project in machine learning
- Collaboration at many levels: Computer Vision Hackathon run with academic partner the Computer Vision for Human-Computer Interaction Lab
Machine learning: success with minimal training data
For his PhD thesis, Simon Reiß is focusing on training data and machine learning methods. Large amounts of image files are needed to perform industrial inspections and make medical diagnoses. The quality of the photographs and the subsequent image analysis are important parts of the process. A manual image assessment would not be possible due to the sheer amount of time needed. Instead, over the last few years machine learning processes have been established for automatic image analysis and processing, particularly with regard to segmentation, detection and classification.
Automatic image segmentation: Learning using small sample datasets
To enable automatic image analysis, artificial intelligence processes must first be used to teach the computer where and what it should look for on the images. For example, a computer can be trained to look for pathologies and anomalies on medical images.
Current methods often have to focus on many accurately annotated image samples in order to teach the algorithm how to perform complex tasks. To learn how to automatically segment brain tumors on MRI scans, a machine learning algorithm needs to view a large number of brain MRI scans on which experts have marked the tumorous tissue. These are known as annotated training data. Since data annotations are complicated and time-consuming, this kind of training data are usually only available to a limited extent. Particularly when the algorithm is to split images into meaningful segments, marked images are needed to train the algorithms one pixel at a time.
The aim of his thesis project, titled "Image segmentation in small datasets with few annotations," is to develop a machine learning method whereby the algorithm only requires little training data or annotations to deliver a high-quality result.
Detecting diseases early on
We can already assess images of the eye's retina to diagnose diseases early on. To do this, experts must rely on OCT scans. These are produced using the latest image capturing procedures and are a laborious way to perform searches on the images to interpret any anomalous structures. An auxiliary system pre-processes these scans for the experts and segments any signs of disease. This way, the experts can immediately focus on any diseased tissue. Thanks to machine learning, image processing tools are already capable of doing this.
These are two areas Simon is focusing on in his thesis project. How does a system like this work with a small dataset? And: How can machine learning methods learn from less precise annotations, thus demanding much less from the experts who compile them?
Simon's PhD thesis is a joint project run by the Computer Vision for Human-Computer Interaction Lab (CV:HCI) at KIT and ZEISS. Simon Reiß benefits from his proximity to the ZEISS Innovation Hub @ KIT and, as a PhD student on the Machine Learning team at ZEISS Corporate Research, is involved with the ZEISS sites in Jena, Oberkochen and Munich. This allows him to both use and advance the machine learning and image processing methods for the ZEISS Group.
"The international ideas sharing, the wonderful tendency of industry and research institutes to share their findings and insights, and the rapid changes are what fascinate me about the Computer Vision Community", says Reiß.
The "Computer Vision Hackathon" at the ZEISS Innovation Hub @ KIT
The collaboration with the CV:HCI Lab at KIT goes well beyond the initial joint PhD thesis. The ZEISS Innovation Hub Computer Vision Hackathon will run from 25 to 27 September. The Laboratory acts as an academic partner. At the hackathon, ambitious and creative individuals come face to face with real-life challenges that data experts and image processing specialists at ZEISS and the KIT research group have to deal with. These range from virtually trying on glasses to helping a visually impaired person navigate a crowded area.
Head of Corporate Brand and Communications
ZEISS is an internationally leading technology enterprise operating in the optics and optoelectronics industries. In the previous fiscal year, the ZEISS Group generated annual revenue totaling more than 6.4 billion euros in its four segments Industrial Quality & Research, Medical Technology, Consumer Markets and Semiconductor Manufacturing Technology (updated: 30 September 2019).
For its customers, ZEISS develops, produces and distributes highly innovative solutions for industrial metrology and quality assurance, microscopy solutions for the life sciences and materials research, as well as medical technology solutions for diagnostics and treatment in ophthalmology and microsurgery. The name ZEISS is also synonymous with the world's leading lithography optics, which are used by the chip industry to manufacture semiconductor components. There is global demand for trendsetting ZEISS brand products such as eyeglass lenses, camera lenses and binoculars.
With a portfolio aligned with future growth areas like digitalization, healthcare and Industry 4.0 as well as a strong brand, ZEISS is shaping technological progress and, through its solutions, is extending the horizon of the world of optics and associated areas. The company's significant, sustainable investments in research and development lay the foundation for the success and continued expansion of ZEISS' technology and market leadership.
With over 31,000 employees, ZEISS is active globally in almost 50 countries with around 60 sales and service companies, 30 production sites and 25 development sites. Founded in 1846 in Jena, the company is headquartered in Oberkochen, Germany. The Carl Zeiss Foundation, one of the largest foundations in Germany committed to the promotion of science, is the sole owner of the holding company, Carl Zeiss AG
Further information at www.zeiss.com