Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Lesion Detectionobject-detectionObject DetectionRegion ProposalSimilar Papers 제목 키워드 기반
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimic…
ClusteringDiabetic Retinopathy DetectionImproving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publ…
Generative Adversarial NetworkLesion SegmentationSegmentationSemantic SegmentationA Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening
Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision p…
DecoderImage AugmentationExplainable Diabetic Retinopathy Detection and Retinal Image Generation
Though deep learning has shown successful performance in classifying the label and severity stage of certain diseases, most of them give few explanations on how to make predictions. Inspired by Koch's Postulates, the fou…
Data AugmentationDiabetic Retinopathy DetectionImage GenerationInterpretable Machine Learning+2Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing
In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well trea…
Dimensionality Reduction