PhD Candidate Durgesh Kumar Singh
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PhD Candidate Durgesh Kumar Singh

"Engaging with experienced researchers and healthcare professionals continuously sharpens my methods and broadens my perspective on the significance of my work"

Durgesh Kumar Singh is a PhD candidate at Visual Intelligence (VI) in Tromsø. He visited the University of Oslo and GE Vingmed Ultrasound from May to June 2024.

"Engaging with experienced researchers and healthcare professionals continuously sharpens my methods and broadens my perspective on the significance of my work"

Durgesh Kumar Singh is a PhD candidate at Visual Intelligence in Tromsø. He visited the University of Oslo and GE Vingmed Ultrasound from May to June 2024.

By Petter Bjørklund, Communication Advisor at SFI Visual Intelligence

Can you tell us about your PhD project

My research focuses on the development and application of deep learning models for computer vision, aiming to enhance our understanding of what these models learn and how they can effectively communicate their knowledge.

I am particularly involved in medical image analysis, specifically deep landmark detection for left ventricular (LV) linear measurements. This research aims to improve the accuracy and efficiency of measuring cardiac structures, which is essential for diagnosing and treating heart conditions. By leveraging advanced deep learning techniques, I work to develop models that can reliably identify key anatomical landmarks in medical images, facilitating better clinical decision-making.

Could you describe the collaborative efforts surrounding your project?

Based on close collaboration with user partners such as GE Vingmed, Rikshospitalet in Oslo, and various researchers at UiO, we are addressing pressing issues in LV linear measurements. These measurements are critical indicators for diagnosing heart conditions and have significant benefits for patient care.

We have developed a novel technique for LV linear measurements that not only overcomes the limitations of previous methods but is also more medically relevant. Our collaborative efforts extend to exploring ideas for the automatic detection of significant cardiac events, learning with synthetic data, and adapting synthetically learned models to real data.

This approach helps mitigate the challenge of limited training data for LV linear measurements, ensuring that our models can perform effectively despite the scarcity of labeled training data. We are still researching the synthetic data problem to further improve our models.

The collaborative and supportive environment at the University of Oslo has greatly enhanced my grasp of both the theoretical and practical facets of my research. Engaging with experienced researchers and healthcare professionals continuously sharpens my methods and broadens my perspective on the significance of my work. This blend of academic insight and practical application allows me to thoroughly understand the challenges and opportunities within the field of medical image analysis, ensuring my research is both relevant and impactful.

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