  The extensive use of medical CT has raised a public concern over the
radiation dose to the patient. Reducing the radiation dose leads to increased
CT image noise and artifacts, which can adversely affect not only the
radiologists judgement but also the performance of downstream medical image
analysis tasks. Various low-dose CT denoising methods, especially the recent
deep learning based approaches, have produced impressive results. However, the
existing denoising methods are all downstream-task-agnostic and neglect the
diverse needs of the downstream applications. In this paper, we introduce a
novel Task-Oriented Denoising Network (TOD-Net) with a task-oriented loss
leveraging knowledge from the downstream tasks. Comprehensive empirical
analysis shows that the task-oriented loss complements other task agnostic
losses by steering the denoiser to enhance the image quality in the task
related regions of interest. Such enhancement in turn brings general boosts on
the performance of various methods for the downstream task. The presented work
may shed light on the future development of context-aware image denoising
methods.
