SUNet: A Lesion Regularized Model for Simultaneous Diabetic Retinopathy and Diabetic Macular Edema Grading

Zhi, Tu; Gao, Shenghua; Zhou, Kang; Chen, Xianing; Fu, Huazhu; Gu, Zaiwang; Cheng, Jun; Yu, Zehao; Liu, Jiang

doi:10.1109/isbi45749.2020.9098673

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…To the best of our knowledge, since the competition covers both DR and DME, ref. [39] is the only non-competition research result that provides independent DR grading so far, we list its quantitative results as well. It should be pointed out that our method does not use additional data for pre-training or relying on model ensembles like other solutions.…”

Section: Results On Idrid Datasetmentioning

confidence: 99%

Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation

2020

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Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness, which plagues the working-age population. Automatic medical imaging diagnosis provides a non-invasive method to assist ophthalmologists in timely screening of suspected DR cases, which prevents its further deterioration. However, the state-of-the-art deep-learning-based methods generally have a large amount of model parameters, which makes large-scale clinical deployment a time-consuming task. Moreover, the severity of DR is associated with lesions, and it is difficult for the model to focus on these regions. In this paper, we propose a novel deep-learning technique for grading DR with only image-level supervision. Specifically, we first customize the model with the help of self-knowledge distillation to achieve a trade-off between model performance and time complexity. Secondly, CAM-Attention is used to allow the network to focus on discriminative zone, e.g., microaneurysms, soft/hard exudates, etc.. Considering that directly attaching a classifier after the Side branch will disrupt the hierarchical nature of convolutional neural networks, a Mimicking Module is employed that allows the Side branch to actively mimic the main branch structure. Extensive experiments are conducted on two benchmark datasets, with an AUC of 0.965 and an accuracy of 92.9% for the Messidor dataset and 67.96% accuracy achieved for the challenging IDRID dataset, which demonstrates the superior performance of our proposed method.

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Section: Results On Idrid Datasetmentioning

confidence: 99%

Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation

2020

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“…The diagnosis of the two diseases can be mutually enhanced. Tu et al (2020) proposed a multi-task network named feature Separation and Union Network (SUNet) for simultaneous DR and DME grading. Experiments were carried on the IDRiD dataset.…”

Section: Diagnosis Of Multiple Diseasesmentioning

confidence: 99%

Applications of Deep Learning in Fundus Images: A Review

Li¹,

Bo²,

Hu³

et al. 2021

Preprint

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The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus Review to adapt to the rapid development of this field.

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“…First, the most valuable task is to predict diabetic retinopathy progression (i.e. grading [1], [8]- [12]). Gulshan et al [1] adopted the Inception-v3 architecture to train a DR grading model, which aims to directly learn the local features rather than explicitly detecting lesions.…”

Section: Introductionmentioning

confidence: 99%

A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability

Zhou,

Wang,

Huang

et al. 2020

Preprint

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People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in the retina. Computer-aided DR diagnosis has become a promising tool for the early detection and severity grading of DR, due to the great success of deep learning. However, most current DR diagnosis systems do not achieve satisfactory performance or interpretability for ophthalmologists, due to the lack of training data with consistent and fine-grained annotations. To address this problem, we construct a large fine-grained annotated DR dataset containing 2,842 images (FGADR). Specifically, this dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists with intra-rater consistency. The proposed dataset will enable extensive studies on DR diagnosis. Further, we establish three benchmark tasks for evaluation: 1. DR lesion segmentation; 2. DR grading by joint classification and segmentation; 3. Transfer learning for ocular multi-disease identification. Moreover, a novel inductive transfer learning method is introduced for the third task. Extensive experiments using different state-of-the-art methods are conducted on our FGADR dataset, which can serve as baselines for future research. Our dataset will be released in https://github.com/csyizhou/FGADR-2842-Dataset.

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SUNet: A Lesion Regularized Model for Simultaneous Diabetic Retinopathy and Diabetic Macular Edema Grading

Cited by 15 publications

References 10 publications

Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation

Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation

Applications of Deep Learning in Fundus Images: A Review

A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability

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