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I-X Research Presentations: Guang Yang

Key Details:

Time: 15.30-16.30
Date: Tuesday 27 August
Location: In Person | I-X Conference Room |  Level 5
Translation and Innovation Hub (I-HUB)
Imperial White City Campus
84 Wood Lane
London W12 0BZ

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Speaker

Dr Guang Yang

Dr Guang Yang is an Associate Professor (Senior Lecturer) in the Bioengineering Department and Imperial-X at Imperial College London. He holds a UKRI Future Leaders Fellowship and serves as an Honorary Senior Lecturer in the School of Biomedical Engineering & Imaging Sciences at King’s College London. He is an Associate Editor of IEEE Transactions and npj Digital Medicine. His research group is dedicated to developing novel and translational techniques for imaging and biomedical data analysis. The group’s focus encompasses research and development in data-driven fast imaging, data harmonization, data synthesis, federated learning, explainable AI, and AI in drug discovery. Currently, his work spans a wide range of clinical applications in ageing, cardiovascular disease, lung disease, and oncology. For more information about Yang’s Lab, visit: https://www.yanglab.fyi/ and Twitter: @gyangMedIA

Talk Summary

Medical image analysis is a critical field that bridges the gap between raw medical imaging data and actionable clinical insights. This talk, “Completing the Loop: Medical Image Analysis from Segmentation to Reconstruction and Back,” delves into the comprehensive journey of medical images through the stages of segmentation, reconstruction, and their iterative interplay. Starting with segmentation, we explore how precise delineation of anatomical structures and pathological regions forms the cornerstone of accurate diagnosis and treatment planning. Some SOTA deep learning based methods will be introduced. The journey continues with image reconstruction, where fast imaging and image quality control are crucial. The loop is completed by revisiting segmentation and quantitative imaging biomarker extraction with refined and reconstructed images. Through this comprehensive cycle, we aim to demonstrate how closing the loop between segmentation and reconstruction leads to a more robust, accurate, and clinically valuable medical image analysis pipeline.

Research Group

YangLab

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