Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e. g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected failures or excessive conservativism. In this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with FEM and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly variable. Results demonstrated that the “H1-L1” neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

StaSiS-Net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3D laparoscopy

Developing accurate and real-time algorithms for a non-invasive three-dimensional representation and reconstruction of internal patient structures is one of the main research fields in computer-assisted surgery and endoscopy. Mono and stereo endoscopic images of soft tissues are converted into a three-dimensional representation by the estimation of depth maps. However, automatic, detailed, accurate and robust depth map estimation is a challenging problem that, in the stereo setting, is strictly dependent on a robust estimate of the disparity map. Many traditional algorithms are often inefficient or not accurate. In this work, novel self-supervised stacked and Siamese encoder/decoder neural networks are proposed to compute accurate disparity maps for 3D laparoscopy depth estimation. These networks run in real-time on standard GPU-equipped desktop computers and the outputs may be used for depth map estimation using the a known camera calibration. We compare performance on three different public datasets and on a new challenging simulated dataset and our solutions outperform state-of-the-art mono and stereo depth estimation methods. Extensive robustness and sensitivity analyses on more than 30000 frames has been performed. This work leads to important improvements in mono and stereo real-time depth map estimation of soft tissues and organs with a very low average mean absolute disparity reconstruction error with respect to ground truth

Cross X-AI: Explainable Semantic Segmentation of Laparoscopic Images in Relation to Depth Estimation

In this work, two deep learning models, trained to segment the liver and perform depth reconstruction, are compared and analysed with their post-hoc explanation interplay. The first model (a U-Net) is designed to perform liver semantic segmentation over different subjects and scenarios. Particularly, the image pixels representing the liver are classified and separated by the surrounding pixels. Meanwhile, with the second model, a depth estimation is performed to regress the z-position of each pixel (relative depths). In general, these two models apply a sort of classification task which can be explained for each model individually and that can be combined to show additional relations and insights between the most relevant learned features. In detail, this work shows how post-hoc explainable AI systems (X-AI) based on Grad CAM and Grad CAM++ can be compared by introducing Cross X-AI (CX-AI). Typically the post-hoc explanation maps provide different visual explanations of their decisions based on the two proposed approaches. Our results show that the Grad Cam++ segmentation explanation maps present cross-learning strategies similar to disparity explanations (and vice versa)

Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e. g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected failures or excessive conservativism. In this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with FEM and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly variable.Results demonstrated that the "H1-L1" neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive

Machine Learning as a Support for the Diagnosis of Type 2 Diabetes

Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time

Motor strength classification with machine learning approaches applied to anatomical neuroimages

Pattern recognition methods for classification are leveraged in the field of computational anatomy and neuroimaging showing high reliability and applicability. Body-brain human functions related to the motor-strength features can be discovered by data integration and analysis of 3D brain images, phenotype and behavioural information. This work is focused on the study of feature-based interplay of 3D brain structures with motor-strength information. In particular, this research introduces an ensemble of supervised machine learning approaches for a binary motor-strength classification (strong vs weak) based on 3D brain anatomical features. The proposed approach has been evaluated on 1113 case studies by obtaining well-defined features and reaching the average accuracy of 72% on the test set

A comparative analysis of multi-backbone Mask R-CNN for surgical tools detection

Real-time surgical tool segmentation and tracking based on convolutional neural networks (CNN) has gained increasing interest in the field of mini-invasive surgery. In fact, the application of this novel artificial vision technologies allows both to reduce surgical risks and to increase patient safety. Moreover, these types of models can be used both to track the tools and detect markers or external artefacts in a real-time video stream. Multiple object detection and instance segmentation can be addressed efficiently by leveraging region-based CNN models. Thus, this work provides a comparison among state-of-the-art multi-backbone Mask R-CNNs to solve these tasks. Moreover, we show that such models can serve as a basis for tracking algorithms. The models were trained and tested with a data-set of 4955 manually annotated images, validated by 3 experts in the field. We tested 12 different combinations of CNN backbones and training hyperparameters. The results show that it is possible to employ a modern CNN to tackle the surgical tool detection problem, with the best-performing Mask R-CNN configuration achieving 87% Average Precision (AP) at Intersection over Union (IOU) 0.5

Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers

In recent years, there have been several proposed applications based on Convolutional Neural Networks (CNN) to neuroimaging data analysis and explanation. Traditional pipelines require several processing steps for feature extraction and ageing biomarker detection. However, modern deep learning strategies based on transfer learning and gradient-based explanations (e.g., Grad-Cam++) can provide a more powerful and reliable framework for automatic feature mapping, further identifying 3D ageing biomarkers. Despite the existence of several 3D CNN methods, we show that a LeNet-like 2D-CNN model trained on T1-weighted MRI images can be used to predict brain biological age in a classification task and, by transfer learning, in a regression task. In addition, automatic averaging and aligning of 2D-CNN gradient-based images is applied and shown to improve its biological meaning. The proposed model predicts soft biological brain ageing indicators with a six-class-balanced accuracy of ≈ 70 % by using the anagraphic age of 1100 healthy subjects in comparison to their brain scans