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Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine requires “unlocking” embedded information captured by different data modalities, in an intuitive and generalizable fashion. The research in my group focuses on developing novel computational imaging features (termed “radiomic” features) together with histology or molecular data for disease characterization and treatment response evaluation in vivo. We have also developed tools and approaches towards enabling these AI models to be repeatable across imaging parameters as well as reproducible across site or scanner variations. Specific problems being addressed by us include: (a) predicting response to treatment to identify optimal therapeutic pathways, as well as (b) evaluating therapeutic response to guide follow-up procedures; in the context of clinical applications in colorectal cancers and digestive diseases. |
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