Abstract
Landmark localisation in medical imaging has achieved great success using deep encoder-decoder style networks to regress heatmap images centered around the target landmarks. However, these networks are large and computationally expensive. Moreover, their clinical use often requires human interaction, opening the door for manual correction of low confidence predictions. We propose PHD-Net: a lightweight, multi-task Patch-based network combining Heatmap and Displacement regression. We design a simple Candidate Smoothing strategy to fuse its two-task outputs, generating the final prediction with quantified confidence. We evaluate PHD-Net on hundreds of Short Axis and Four Chamber cardiac MRIs, showing promising results.
Lawrence Schobs
PhD Student
Shuo Zhou
Academic Fellow at University of Sheffield (past PhD Student)
Haiping Lu
Director of the UK Open Multimodal AI Network, Professor of Machine Learning, and Head of AI Research Engineering
I am a Professor of Machine Learning. I develop translational multimodal AI technologies for advancing healthcare and scientific discovery.