Development of low-overhead soft error mitigation technique for safety critical neural networks applications

Deep Neural Networks (DNNs) have been widely applied in healthcare applications. DNN-based healthcare applications are safety-critical systems that require highreliability implementation due to a high risk of human death or injury in case of malfunction. Several DNN accelerators are used to execute these DNN models, and GPUs are currently the most prominent and the dominated DNN accelerators. However, GPUs are prone to soft errors that dramatically impact the GPU behaviors; such error may corrupt data values or logic operations, which result in Silent Data Corruption (SDC). The SDC propagates from the physical level to the application level (SDC that occurs in hardware GPUs’ components) results in misclassification of objects in DNN models, leading to disastrous consequences. Food and Drug Administration (FDA) reported that 1078 of the adverse events (10.1%) were unintended errors (i.e., soft errors) encountered, including 52 injuries and two deaths. Several traditional techniques have been proposed to protect electronic devices from soft errors by replicating the DNN models. However, these techniques cause significant overheads of area, performance, and energy, making them challenging to implement in healthcare systems that have strict deadlines. To address this issue, this study developed a Selective Mitigation Technique based on the standard Triple Modular Redundancy (S-MTTM-R) to determine the model’s vulnerable parts, distinguishing Malfunction and Light-Malfunction errors. A comprehensive vulnerability analysis was performed using a SASSIFI fault injector at the CNN AlexNet and DenseNet201 models: layers, kernels, and instructions to show both models’ resilience and identify the most vulnerable portions and harden them by injecting them while implemented on NVIDIA’s GPUs. The experimental results showed that S-MTTM-R achieved a significant improvement in error masking. No-Malfunction have been improved from 54.90%, 67.85%, and 59.36% to 62.80%, 82.10%, and 80.76% in the three modes RF, IOA, and IOV, respectively for AlexNet. For DenseNet, NoMalfunction have been improved from 43.70%, 67.70%, and 54.68% to 59.90%, 84.75%, and 83.07% in the three modes RF, IOA, and IOV, respectively. Importantly, S-MTTMR decreased the percentage of errors that case misclassification (Malfunction) from 3.70% to 0.38% and 5.23% to 0.23%, for AlexNet and DenseNet, respectively. The performance analysis results showed that the S-MTTM-R achieved lower overhead compared to the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR). In light of these results, the study revealed strong evidence that the developed S-MTTMR was successfully mitigated the soft errors for the DNNs model on GPUs with lowoverheads in energy, performance, and area indicated a remarkable improvement in the healthcare domains’ model reliability

Detection of central serous retinopathy using deep learning through retinal images

The human eye is responsible for the visual reorganization of objects in the environment. The eye is divided into different layers and front/back areas; however, the most important part is the retina, responsible for capturing light and generating electrical impulses for further processing in the brain. Several manual and automated methods have been proposed to detect retinal diseases, though these techniques are time-consuming, inefficient, and unpleasant for patients. This research proposes a deep learning-based CSR detection employing two imaging techniques: OCT and fundus photography. These input images are manually augmented before classification, followed by training of DarkNet and DenseNet networks through both datasets. Moreover, pre-trained DarkNet and DenseNet classifiers are modified according to the need. Finally, the performance of both networks on their datasets is compared using evaluation parameters. After several experiments, the best accuracy of 99.78%, the sensitivity of 99.6%, specificity of 100%, and the F1 score of 99.52% were achieved through OCT images using the DenseNet network. The experimental results demonstrate that the proposed model is effective and efficient for CSR detection using the OCT dataset and suitable for deployment in clinical applications.This work was supported by the Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad Campus, Pakistan. Open Access funding provided by the Qatar National Library. Qatar National Library and Qatar University Internal Grant IRCC-2021–010 funded this work

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho