End-to-End Deep Learning Image Reconstruction and Pathology Detection

Third party funded individual grant

Start date : 01.01.2023

End date : 31.12.2025

Project details

Short description

The majority of diagnostic medical imaging pipelines follow the same principles: raw measurement data is acquired by scanner hardware, processed by image reconstruction algorithms, and then evaluated for pathology by human radiology experts. Under this paradigm, every step has traditionally been optimized to generate images that are visually pleasing and easy to interpret for human experts. However, raw sensor information that could maximize patient-specific diagnostic information may get lost in this process. This problem is amplified by recent developments in machine
learning for medical imaging. Machine learning has been used successfully in all steps of the diagnostic imaging pipeline: from the design of data acquisition to image reconstruction, to computer-aided diagnosis. So far, these developments have been disjointed from each other. In this project, we will fuse machine learning for image reconstruction and for image-based disease localization, thus providing an end-to-end learnable image reconstruction and joint pathology detection approach that operates directly on raw measurement data. Our hypothesis is that this combination can maximize diagnostic accuracy while providing optimal images for both human experts and diagnostic machine learning models.


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