Multi-Modal Multi-Instance Learning for Retinal Disease Recognition

Li, Xirong; Yang, Zhou; Wang, Jie; Lin, Hailan; Zhao, Jianchun; Ding, Dayong; Wu, Yu; Chen, Youxin

doi:10.1145/3474085.3475418

Cited by 19 publications

(7 citation statements)

References 22 publications

(26 reference statements)

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“…However, this creates representational bottlenecks because the demonstration's overall dimensionality decreases to

{\left(\frac{l}{2}\right)}^2k

minus communicative network. Instead of using this, another option is suggested that decreases the calculation rate even more by reducing the representative bottleneck 2 …”

Section: Proposed Methodologymentioning

confidence: 99%

“…Retinitis pigmentosa, cataract, ocular surface neoplasia, age‐related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and pterygium are the most common retinal illnesses. Aging, smoking, diabetes, obesity, cardiovascular disease, genetics, and other factors all contribute to an increased risk of progression and development of these diseases 2 . The majority of ocular illnesses affect both eyes, with 80% of all causes of visual impairment curable if detected earlier.…”

Section: Introductionmentioning

confidence: 99%

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Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Jain,

Bhatnagar,

Rao

et al. 2023

Computational Intelligence

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Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre‐processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception‐ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.

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“…However, this creates representational bottlenecks because the demonstration's overall dimensionality decreases to

{\left(\frac{l}{2}\right)}^2k

minus communicative network. Instead of using this, another option is suggested that decreases the calculation rate even more by reducing the representative bottleneck 2 …”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Jain,

Bhatnagar,

Rao

et al. 2023

Computational Intelligence

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show abstract

“…The success of deep learning based visual recognition in various applications has stimulated interest in solving retinal disease classification tasks from multi-modal images [ 14 , 15 , 19 – 23 ]. Yoo et al [ 20 ] employed random forests and VGG networks to extract features from OCT and CFP images, and then experimented with feature concatenation to aid in the multi-modal image diagnosis of AMD.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al [ 22 ] designed a vertical plane feature fusion method for multi-modal fusion to predict AMD disease use infrared reflectance and OCT images. Li et al [ 15 ] proposed a multi-modal multi-instance Learning deep learning framework using CFP and OCT, selectively fusing CFP and OCT modalities feature. Song et al [ 23 ] developed a multi-modal information bottleneck network (MMIB-Net) leveraging information bottleneck theory for feature representation in multiple modalities.…”

Section: Related Workmentioning

confidence: 99%

Cross-modal attention network for retinal disease classification based on multi-modal images

Liu,

Hu,

Qiu

et al. 2024

Biomed. Opt. Express

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Multi-modal eye disease screening improves diagnostic accuracy by providing lesion information from different sources. However, existing multi-modal automatic diagnosis methods tend to focus on the specificity of modalities and ignore the spatial correlation of images. This paper proposes a novel cross-modal retinal disease diagnosis network (CRD-Net) that digs out the relevant features from modal images aided for multiple retinal disease diagnosis. Specifically, our model introduces a cross-modal attention (CMA) module to query and adaptively pay attention to the relevant features of the lesion in the different modal images. In addition, we also propose multiple loss functions to fuse features with modality correlation and train a multi-modal retinal image classification network to achieve a more accurate diagnosis. Experimental evaluation on three publicly available datasets shows that our CRD-Net outperforms existing single-modal and multi-modal methods, demonstrating its superior performance.

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“…Recently, deep learning-based methods, such as image classification, objection detection and semantic segmentation, have been applied successfully on OCT images to solve various tasks including retinal layer segmentation [1,5], lesion localization [3] and multimodal retinal disease recognition [8,17,18], to name just a few. As these methods follow a standard supervised learning paradigm, a considerable amount of labeled data is often required.…”

Section: Introductionmentioning

confidence: 99%

Lesion Localization in OCT by Semi-Supervised Object Detection

Wu,

Zhou,

Zhao

et al. 2022

Preprint

Self Cite

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Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semisupervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and structured viewpoint of the current SSOD methods, and consequently identify key modules in these methods. To evaluate the influence of these modules in the new task, we build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify Unbiased Teacher (UnT) as the best current SSOD method for lesion localization. Moreover, we improve over this strong baseline, with mAP increased from 49.34 to 50.86.

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Multi-Modal Multi-Instance Learning for Retinal Disease Recognition

Cited by 19 publications

References 22 publications

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Cross-modal attention network for retinal disease classification based on multi-modal images

Lesion Localization in OCT by Semi-Supervised Object Detection

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