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General Electric Healthcare

GE Healthcare is the $16.7 billion healthcare business of GE. As a leading global medical technology and digital solutions innovator, GE Healthcare enables clinicians to make faster, more informed decisions through intelligent devices, data analytics, applications and services, supported by its Edison intelligence platform. With over 100 years of experience in the healthcare industry and more than 50,000 employees globally, the company helps improve outcomes more efficiently for patients, healthcare providers, researchers and life sciences companies around the world. GE Healthcare in Hungary employs around 700 people, mainly highly qualified, innovative engineers who work together to shape the future of medical imaging, workflow management and usage of Artificial Intelligence in healthcare. GE Healthcare opened its Software Development center in 2000 in Budapest then an office in Szeged, followed by GE’s first and only European Industrial Internet Software Centre of Excellence in Budapest. The center is in the forefront of GE Healthcare’s Edison initiative and delivers services to support data management and analytics. GE Healthcare’s largest European Data Science team in Hungary is using cutting edge Artificial Intelligence and Deep Learning techniques to research and deliver better, faster and more effective diagnosis, treatment and care delivery. GE Healthcare announced recently the first FDA approved artificial intelligence (AI) algorithms embedded on a mobile X-ray device developed by the Hungarian GE Healthcare professionals using the GE Healthcare’s Edison platform. The AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung.

David Varkonyi

David graduated summa cum laude from ELTE, one of the biggest Hungarian universities, studying computer science and specializing in numerical modelling. After acquiring his master’s degree in 2015 he stayed at the university for a year, to teach various courses in mathemathics and computer science, and writing publications in a wide area of fields, such as cryptography, celestial mechanics, and digital cartography. After leaving the university, he joined a Hungarian startup, as an AI researcher, to help develop algorithms for the automotive industry, which may power self-driving cars in the future. He joined GE Healthcare in 2019, as a staff data scientist. Being skilled in Python and C++, many data science related frameworks, such as TensorFlow, PyTorch, Keras, having many years of experience in image processing algorithms and GPGPU computing, he currently serves as technical lead for ultrasound related projects.

Edina Timkó

Edina graduated with honour from Pázmány Péter Catholic University in 2020, where she studied the interdisciplinary field of info-bionics engineering, embracing electircal engineering, computer science and biotechnology. She specialized in bionic interfaces. First year into her master studies, in 2019, she has joined GE Healthcare as a data science intern, where she worked with electronic healthcare records for the purpose of precision medicine. She also wrote her master thesis within GE about brain tumor segmentation on multiparametric MRI sequences. After graduation she landed a role as a junior data scientist. She has experience in Python programming and frameworks used for data science related work on both imaging and non-imaging data. As a woman she welcomes the opportunity to work on ultrasound related projects focusing on women’s health.

Eszter Csernai

I am a Data Scientist with 10+ years of experience across industries – Financial Services, Social Science Research, IT Security.  In the past 5 years, I have been in Healthcare with GE Healthcare, and this is where I found my passion. I have worked on brain segmentation in MR images using deep learning, a precision oncology project analyzing risk of adverse effects of immunotherapy cancer drugs, and optimizing hospital workflow by predicting no-shows for advanced imaging exams, among other smaller projects. My educational background is Quantitative Economics, Operations Research, Statistics, Linguistics. My main interest is using state-of-the-art AI technology to improve medical decision making and patient experience.

Martin Mienkina

Martin Mienkina leads the Advanced Technology Development for the Women’s Health Ultrasound business unit within GE Healthcare. He has been working and providing leadership at the intersection of research and development in the field of ultrasound imaging and workflow automation for more than a decade. He received a doctorate in Electrical Engineering, focusing on ultrasound imaging, from the Ruhr-University Bochum Germany in 2010.

Lehel Ferenczi

I am leading a Data Science team in GE Healthcare Hungary formed from data scientists, data analysts, software engineers and clinicians. I am passionate about healthcare, software development and machine learning. I have been working in the healthcare IT sector since 2001, mainly in oncology, neurology and radiology. Over the years, I have gained a wealth of experience in the health sector (product, compliance, regulation, risk management, standards). I have experience in the development of health products from wing to wing, from idea to delivery.
I graduated in Computer Programming and Automation from Timişoara Technical University (Temesvár) and studied post-graduate studies in computer controlled geometric design at the Technical University of Budapest.
From 2001, I led the development of a number of oncology/radiology applications and products for 14 years applications such as AdvantageSim MD, Integrated Registration (Fusion), Advantage 4D (CT, PET / CT).
From 2014 to 2017, I worked on innovative healthcare solutions as part of a consortium of scientists, university professors, clinical partners and GE Healthcare as an industrial partner, funded by a Hungarian national grant.  Within this consortium, my team and I have been able to develop innovative solutions that have made us to be part of GE Healthcare’s data research center since more than 2 years.
Since 2016 I am leading a data science team of over 30 experts dedicated to deliver machine and deep learning solutions for a variety of medical imaging techniques (CT, MR, PET, ultrasound and X-ray).