Deep multi-task learning for nephropathy diagnosis on immunofluorescence images.
Journal:
Computer methods and programs in biomedicine
PMID:
37619430
Abstract
BACKGROUND AND OBJECTIVE: As an advanced technique, immunofluorescence (IF) is one of the most widely-used medical image for nephropathy diagnosis, due to its ease of acquisition with low cost. In practice, the clinically collected IF images are commonly corrupted by blurs at different degrees, mainly because of the inaccurate focus at the acquisition stage. Although deep neural network (DNN) methods achieve the great success in nephropathy diagnosis, their performance dramatically drops over the blurred IF images. This significantly limits the potential of leveraging the advanced DNN techniques in real-world nephropathy diagnosis scenarios.