Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features

11/19/2019
by   Qilei Chen, et al.
0

Diabetic retinopathy (DR) is a diabetes complication that affects eyes. DR is a primary cause of blindness in working-age people and it is estimated that 3 to 4 million people with diabetes are blinded by DR every year worldwide. Early diagnosis have been considered an effective way to mitigate such problem. The ultimate goal of our research is to develop novel machine learning techniques to analyze the DR images generated by the fundus camera for automatically DR diagnosis. In this paper, we focus on identifying small lesions on DR fundus images. The results from our analysis, which include the lesion category and their exact locations in the image, can be used to facilitate the determination of DR severity (indicated by DR stages). Different from traditional object detection for natural images, lesion detection for fundus images have unique challenges. Specifically, the size of a lesion instance is usually very small, compared with the original resolution of the fundus images, making them diffcult to be detected. We analyze the lesion-vs-image scale carefully and propose a large-size feature pyramid network (LFPN) to preserve more image details for mini lesion instance detection. Our method includes an effective region proposal strategy to increase the sensitivity. The experimental results show that our proposed method is superior to the original feature pyramid network (FPN) method and Faster RCNN.

READ FULL TEXT

page 1

page 2

page 4

research
03/26/2020

Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a primary cause of blindness in working-age...
research
05/02/2017

Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

We propose an automatic diabetic retinopathy (DR) analysis algorithm bas...
research
03/26/2016

Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework

Large medical image data sets with high dimensionality require substanti...
research
12/27/2022

Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment

People with diabetes are more likely to develop diabetic retinopathy (DR...
research
12/10/2019

DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a complication of diabetes that severely af...
research
03/02/2021

An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images

Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Ye...
research
04/05/2017

Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features

All people with diabetes have the risk of developing diabetic retinopath...

Please sign up or login with your details

Forgot password? Click here to reset