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A Human-Inspired Statistical Framework for Tumour Delineation in Medical Imaging
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Develop statistical methods that replicate radiologist perception while enhancing precision in tumour boundary definition.
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Capture 2D/3D spatial dependencies and fuse CT, MRI, PET and other imaging data to maximize complementary insights.
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Deliver efficient, reproducible segmentation workflows validated on clinical datasets and optimized for modern computing environments.
