CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

Li, Xiaomeng; Hu, Xiaowei; Yu, Lequan; Zhu, Lei; Fu, Chi‐Wing; Heng, Pheng‐Ann

doi:10.1109/tmi.2019.2951844

Cited by 319 publications

(120 citation statements)

References 47 publications

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“…EyePACS dataset. To evaluate the transfer learning capacity of our model, we train the self-supervised model on the Kaggle's Diabetic Retinopathy Detection Challenge (EyePACS) dataset 4 and report the classification result on the AMD dataset. This dataset is sponsored by the California Healthcare Foundation.…”

Section: A Datasetsmentioning

confidence: 99%

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Jia

Islam

et al. 2020

IEEE Trans. Med. Imaging

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108

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The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decisionmaking. However, developing such automatic solutions is challenging due to the requirement of a large amount of humanannotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline. Our code is available at GitHub 1. Index Terms-Retinal disease diagnosis, self-supervised learning, multi-modal data I. INTRODUCTION C OLOR fundus photography has been widely used in clinical practice to evaluate various conventional ophthalmic diseases, e.g., age-related macular degeneration (AMD) [1], pathologic myopia (PM) [2], and diabetic retinopathy [3, 4]. Recently, deep learning has shown very good performance on a variety of automatic ophthalmic disease detection problems from fundus images [5-7], and these techniques can help ophthalmologists in decision making. The success is attributed to the learned representative features from fundus images, which requires a large amount of training data with massive human annotations. However, it is tedious and expensive to annotate the fundus images, since experts are needed to provide reliable labels. Hence, in this paper, our goal is to learn the representative features from data itself, without any human

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Section: A Datasetsmentioning

confidence: 99%

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Jia

Islam

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

108

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“…However, in terms of specificity and accuracy, our method performed better than Syed's method and all other methods. Significant difference in terms of sensitivity can be observed against Li et al [62], Lim et al [65], and Rahim et al [66], where this difference ranges from 11-26%. The method Lim et al [65] which showed higher specificity in comparison to the sensitivity shows that the method considered the DME regions as non-DME regions, whereas, the methods, i.e., Rahim et al [66] where sensitivity is higher than the specificity reflect that the method considered even the non-DME regions as DME.…”

Section: Comparative Studiesmentioning

confidence: 83%

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

et al. 2020

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Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation.

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“…XGBoost uses the second-order Taylor expansion to approximate Equation (14). The final objective function can be expressed as follows:…”

Section: Dr Image Classificationmentioning

confidence: 99%

“…They acquired a peak accuracy of 96.25% on the Messidor dataset. Li proposed a Crossdisease Attention Network (CANet) for grading DR and Diabetic Macular Edema [14]. In their study, the authors designed two attention modules: a disease-specific module that utilizes both the inter-spatial and inter-channel relationship among the features and a disease-dependent module that exploits the inter-channel features collected from the retinal images.…”

Section: Introductionmentioning

confidence: 99%

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

et al. 2021

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Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

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CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

Cited by 319 publications

References 47 publications

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

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