Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing

TL;DR

This paper introduces a multi-modal deep guided filtering approach for medical image processing that enhances interpretability, robustness, and content preservation in tasks like super-resolution and denoising, using learned guidance maps within an end-to-end framework.

Contribution

It proposes integrating locally linear guided filters with learned guidance maps into deep learning for medical imaging, improving transparency and robustness over traditional methods.

Findings

Achieves comparable performance to state-of-the-art methods.

Maintains image content integrity after processing.

Increases robustness against degraded inputs and adversarial attacks.

Abstract

Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks.