BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Abstract

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Accepted at ICIP 2019

References in corpus (1)

• Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Cited by in corpus (7)

• Multi-Target Deep Learning for Algal Detection and Classification
• Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
• Deeply Supervised Active Learning for Finger Bones Segmentation
• Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
• Residual Channel Attention Network for Brain Glioma Segmentation
• Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach
• Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma