2023/10/26 | Research |
Introducing saliency feature learning to multi-sequence MRI could improve the segmentation quality of deep learning systems. This is the finding of a recent study of the Medical Image Analysis lab in collaboration with the Support Center for Advanced Neuroimaging (SCAN) and the Department of Radiation Oncology of the Inselspital, Bern University Hospital.
On a cohort of 2100 patient cases comprising six different MR sequences per case, the team tested a novel method that uses saliency information to guide the learning of deep learning features for sequence classification. The method showed an improvement in mean accuracy by 4.4% for segmentation. Based on feedback from an expert neuroradiologist, the proposed approach furthermore improved the interpretability of trained models as well as their calibration with reduced expected calibration error (by 30.8%).
Link to the study
Medical Image Analysis