Glaucoma is a progressive optic neuropathy character-ized by structural damage to the optic nerve head andfunctinoal changes in the visual field. Detecting glaucoma early is crucial to preventing loss of eyesight. However, med-ical datasets often suffer from class imbalances, making detection more difficult for deep-learning algorithms. We use a generative-based framework to enhance glaucoma di-agnosis, specifically addressing class imbalance through synthetic data generation. In addition, we collected the largest national dataset for glaucoma detection to support our study. The imbalance between normal and glaucoma-tous cases leads to performance degradation of classifier models. We created a more robust classifier training process by combining our proposed framework leveraging diffusion models with a pretraining approach. This training process results in a better-performing classifier. The proposed approach shows promising results in improving the harmonic mean “sensitivity and specificity” and AU C for the roc for the glaucoma classifier. We report an improvement in the harmonic mean metric from 89.09% to 92.59% on the test set of our Egyptian dataset. We examine our method against other methods to overcome imbalance through extensive experiments. We report similar improvements on the AIROGS dataset. This study highlights that diffusion-based generatino can be important in tackling class imbalances in medi-cal datasets to improve diagnostic performance.

In this paper, we contrast the usage of two deep-learning approaches for the automatic grading of diabetic retinopathy (DR) and diabetic macular edema (DME) in retinal fundus photographs using a relatively small novel dataset. We developed a telemedicine system to collect and humanly grade 11,109 diabetic patients. The certified graders annotated the level of DR as well as the existence of a referable DME in the macula-centered fundus images only. We use EfficientNet to build an AI-based model for both problems. To examine the transfer learning validity, the model was trained on an external dataset (EyePacs) and then finetuned on the egyptian data for the DR and DME grading problems. Firstly, we use the macula-centered images only in fine-tuning. Secondly, we use optic-disc-centered images in addition to macula-centered images. We obtained the labels for the optic-disc-centered images directly from the corresponding macula-centered labels as weak labels. Then, both types of images are used in fine-tuning. We found an increase in the DR performance using the second approach in both accuracy and quadratic weighted kappa(QWK). Notably, QWK increased from 90.23% to 91.3% using additional weakly labeled optic-disc-centered fundus images.

Convolution Neural Networks (CNN) excelled humans in many classification tasks, including medical imaging applications. However, model interpretation is still an active research area. In this paper, we address the model interpretation problem for the Diabetic Retinopathy grading task. We propose a novel visualization method called StackMaps. Our proposed method is class-agnostic, which fits the diabetic retinopathy grading problem better than other alternatives. Moreover, unlike previous visualization methods, StackMaps gets rid of the dependency on gradients. Our StackMaps technique gets the most significant feature maps at the last convolutional layer after one forward pass. We can also get the significant feature maps from lower layers using beam search guided by the feature maps obtained from the previous layer. Finally, we compute the final map as the sum of the significant feature maps obtained at each layer. We evaluate StackMaps against other state-of-the-art visualization methods qualitatively and quantitatively. We used the FGADR dataset to define our experimental setup. We show that StackMaps achieves better visual interpretation and lesion localization.

Recently, providing explainable deep learning models has sparked a lot of attention. In this paper, we take a further step in this direction. We introduce a time-efficient method, called Ablation-CAM++, which can generate smooth visual explanations of CNN model predictions. Our approach uses the concept of studying the ablation analysis to determine the importance of activation maps w.r.t. the target class, similar to Ablation-CAM. However, instead of focusing on the individual importance of each activation map, we group activation maps using a clustering technique. Then, we construct a binary tree for each group by recursively splitting these groups, studying the ablation of each subgroup, and applying tree pruning. We perform qualitative and quantitative evaluations of our visual explanations against Ablation-CAM and Grad-CAM. Our approach can provide visual explanations in less than half of the time of Ablation-CAM. Using average drop and average increase evaluation metrics on 2000 images of the ImageNet validation set, we provide a comparison of the effect of applying different clustering techniques in our method.

Glaucoma is an eye condition marked by apoptotic ganglion cell death. This condition is primarily caused by increased intraocular pressure (IOP). The clinical examination of the ganglion cell layer is difficult. However, its demise results in distinctive optic nerve alterations. Several clinical examinations are present to diagnose the glaucoma condition. The fundus image captures the optic nerve. Hence, we can apply deep learning models to automatically diagnose the Glaucoma condition in a fundus image. In this paper, we study the classification of the fundus image using a vision transformer-based ensemble. Vision transformers employ self-attention to capture global characteristics of the fundus image. This makes vision transformers a perfect choice for classification problems. We formed one large merged dataset of six publicly available full fundus images for Glaucoma detection. We provide a comprehensive evaluation for more than seven different vision transformer baseline models. Also, we propose an ensemble of the best vision transformer models. We report areas under the curve, sensitivity, and specificity measures due to the class imbalance in the available data. We report the best standalone model achieves 92.57 in sensitivity, 96.94 in specificity, and 97.9 in AUC.

Virtual Reality (VR) immersive environments are becoming more popular and of less cost, hence, VR labs are becoming a main part in any research that depends on visualization. This introduced the need to port many 3D desktop visualization applications to VR. Porting application GUIs can be a problem since original GUIs are 2D by nature and using them directly can obscure a large area of 3D viewport and spoil the immersive experience. On the other hand, rewriting a 3D GUI can be a time consuming and tedious task. In this work, we introduce a technique to embed 2D GUIs into 3D Virtual Environments (VE). Our approach uses existing 2D GUIs that can be immersed into the VE allowing rapid GUI development for VR applications. It can also be used for porting 3D desktop applications without rewriting the GUI code. Further, it enables embedding many window-based desktop applications into the VE, creating rich VEs where users can work with multiple applications simultaneously.

Association rules discovery is an important data mining technique which usually produces large number of rules. Subset and superset queries are common queries for association rules. We introduce a new index structure (SSST) for querying association rules, based on a unique set representation using a hierarchical structure. It supports both Subset and Superset queries. Further, it is scalable and adapts to different types of data. The performance of SSST is evaluated using real as well as synthetic datasets, spanning dense and sparse data. The experiments showed that the proposed structure outperforms other set indexing techniques significantly, especially for dense datasets. Also, it scales well with both the number of association rules and the query size.

This paper describes a fast, reliable, scalable and efficient broadcast protocol called HPP (hierarchical propagation protocol) for weak-consistency replica management. It is based on organizing the nodes in a network into a logical hierarchy and maintaining a limited amount of state information at each node. It ensures that messages are not lost due to failures or partitions and minimizes redundancy. Furthermore, the protocol allows messages to be diffused while nodes are down provided the parent and child nodes of a failed node are alive. Moreover, the protocol allows nodes to be moved in the logical hierarchy and the network to be restructured dynamically in order to improve performance, while still ensuring that no messages are lost while the switch takes place and without disturbing normal operation. A performance study of the protocol in terms of availability and propagation delay indicates that the protocol reduces the delay by a factor of four compared to a protocol that does not diffuse messages past failed nodes.