Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
This work addresses the challenge of adapting medical imaging systems to new technologies for clinicians, but it is incremental as it applies existing translation methods to a specific domain.
The paper tackled the problem of adapting computer-aided decision support systems to new endoscopic imaging modalities, such as white-light and narrow-band imaging, by using image-to-image translation to reduce the need for new labeled data, resulting in improved accuracy for celiac disease diagnosis in data-limited settings.
Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly trained from scratch to exploit the full potential of enhanced techniques. Both options typically require the acquisition of new labeled training data. In this work we investigate the applicability of image-to-image translation to endoscopic images showing different imaging modalities, namely conventional white-light and narrow-band imaging. In a study on computer-aided celiac disease diagnosis, we explore whether image-to-image translation is capable of effectively performing the translation between the domains. We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data. Finally, we also ask whether a translation of testing images to another domain is capable of improving accuracy by exploiting the enhanced imaging characteristics.