286 research outputs found

    Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis

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    HPC is an enabling platform for AI. The introduction of AI workloads in the HPC applications basket has non-trivial consequences both on the way of designing AI applications and on the way of providing HPC computing. This is the leitmotif of the convergence between HPC and AI. The formalized definition of AI pipelines is one of the milestones of HPC-AI convergence. If well conducted, it allows, on the one hand, to obtain portable and scalable applications. On the other hand, it is crucial for the reproducibility of scientific pipelines. In this work, we advocate the StreamFlow Workflow Management System as a crucial ingredient to define a parametric pipeline, called ‚ÄúCLAIRE COVID-19 Universal Pipeline‚ÄĚ, which is able to explore the optimization space of methods to classify COVID-19 lung lesions from CT scans, compare them for accuracy, and therefore set a performance baseline. The universal pipeline automatizes the training of many different Deep Neural Networks (DNNs) and many different hyperparameters. It, therefore, requires a massive computing power, which is found in traditional HPC infrastructure thanks to the portability-by-design of pipelines designed with StreamFlow. Using the universal pipeline, we identified a DNN reaching over 90% accuracy in detecting COVID-19 lesions in CT scans

    Transfer without Forgetting

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    This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes.Comment: 22 pages, 3 Figures. Accepted at 17th European Conference on Computer Vision (ECCV 2022), Tel Aviv, Israe

    Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes

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    This study set out to evaluate the effectiveness of low speed autonomous emergency braking (AEB) technology in current model passenger vehicles, based on real-world crash experience. The Validating Vehicle Safety through Meta-Analysis (VVSMA) group comprising a collaboration of government, industry consumer organisations and researchers, pooled data from a number of countries using a standard analysis format and the established MUND approach. Induced exposure methods were adopted to control for any extraneous effects. The findings showed a 38 percent overall reduction in rear-end crashes for vehicles fitted with AEB compared to a comparison sample of similar vehicles. There was no statistical evidence of any difference in effect between urban (‚ȧ60km/h) and rural (>60km/h) speed zones. Areas requiring further research were identified and widespread fitment through the vehicle fleet is recommended

    An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

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    COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai

    Core-rod myopathy due to a novel mutation in BTB/POZ domain of KBTBD13 manifesting as late onset LGMD

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    Few genes (RYR1, NEB, ACTA1, CFL2, KBTBD13) have been associated with core-rod congenital myopathies [7]. KBTBD13 belongs to the Kelch-repeat super-family of proteins and is implicated in the ubiquitination pathway. Dominant mutations in KBTBD13 have been associated with a peculiar form of core-rod myopathy (NEM6) so far [10]. Childhood onset, slowly progressive proximal muscle weakness with characteristic slowness of movements and combination of nemaline rods, irregular shaped cores and unusual type2 fibres hypotrophy at muscle biopsy, were the main characteristics shared in all the affected members of the four KBTBD13 families reported in the literature [12]. We report on a 65 years old patient, of Sardinian origin, with atypical clinical and morphological presentation of NEM6 due to a novel mutation in KBTBD13 gene

    Core-rod myopathy due to a novel mutation in BTB/POZ domain of KBTBD13 manifesting as late onset LGMD

    Get PDF
    Few genes (RYR1, NEB, ACTA1, CFL2, KBTBD13) have been associated with core-rod congenital myopathies [7]. KBTBD13 belongs to the Kelch-repeat super-family of proteins and is implicated in the ubiquitination pathway. Dominant mutations in KBTBD13 have been associated with a peculiar form of core-rod myopathy (NEM6) so far [10]. Childhood onset, slowly progressive proximal muscle weakness with characteristic slowness of movements and combination of nemaline rods, irregular shaped cores and unusual type2 fibres hypotrophy at muscle biopsy, were the main characteristics shared in all the affected members of the four KBTBD13 families reported in the literature [12]. We report on a 65 years old patient, of Sardinian origin, with atypical clinical and morphological presentation of NEM6 due to a novel mutation in KBTBD13 gene

    Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1) : Refining muscle involvement and implications for clinical trials

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    Only a few studies have reported muscle imaging data on small cohorts of patients with myotonic dystrophy type 1 (DM1). We aimed to investigate the muscle involvement in a large cohort of patients in order to refine the pattern of muscle involvement, to better understand the pathophysiological mechanisms of muscle weakness, and to identify potential imaging biomarkers for disease activity and severity. One hundred and thirty-four DM1 patients underwent a cross-sectional muscle magnetic resonance imaging (MRI) study. Short tau inversion recovery (STIR) and T1 sequences in the lower and upper body were analyzed. Fat replacement, muscle atrophy and STIR positivity were evaluated using three different scales. Correlations between MRI scores, clinical features and genetic background were investigated. The most frequent pattern of muscle involvement in T1 consisted of fat replacement of the tongue, sternocleidomastoideus, paraspinalis, gluteus minimus, distal quadriceps and gastrocnemius medialis. Degree of fat replacement at MRI correlated with clinical severity and disease duration, but not with CTG expansion. Fat replacement was also detected in milder/asymptomatic patients. More than 80% of patients had STIR-positive signals in muscles. Most DM1 patients also showed a variable degree of muscle atrophy regardless of MRI signs of fat replacement. A subset of patients (20%) showed a 'marbled' muscle appearance. Muscle MRI is a sensitive biomarker of disease severity alsofor the milder spectrum of disease. STIR hyperintensity seems to precede fat replacement in T1. Beyond fat replacement, STIR positivity, muscle atrophy and a 'marbled' appearance suggest further mechanisms of muscle wasting and weakness in DM1, representing additional outcome measures and therapeutic targets for forthcoming clinical trials. We refined the pattern of muscle involvement in DM1 by upper and lower body muscle magnetic resonance imaging (MRI), identifying the most frequent pattern of fat replacement and confirming that muscle MRI is a sensitive biomarker of disease burden in DM1. We also observed: STIR-positive muscles in 80% of patients preceding fat replacement, muscle atrophy in muscles unreplaced by fat, and progeroid muscle appearance supporting a premature muscle senescence. Our findings provide novel insights into the pathophysiological mechanisms of muscle wasting and weakness in DM1, and could represent additional outcome measures and therapeutic targets for forthcoming clinical trials

    Lipid Myopathies

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    Disorders of lipid metabolism affect several tissues, including skeletal and cardiac muscle tissues. Lipid myopathies (LM) are rare multi-systemic diseases, which most often are due to genetic defects. Clinically, LM can have acute or chronic clinical presentation. Disease onset can occur in all ages, from early stages of life to late-adult onset, showing with a wide spectrum of clinical symptoms. Muscular involvement can be fluctuant or stable and can manifest as fatigue, exercise intolerance and muscular weakness. Muscular atrophy is rarely present. Acute muscular exacerbations, resulting in rhabdomyolysis crisis are triggered by several factors. Several classifications of lipid myopathies have been proposed, based on clinical involvement, biochemical defect or histopathological findings. Herein, we propose a full revision of all the main clinical entities of lipid metabolism disorders with a muscle involvement, also including some those disorders of fatty acid oxidation (FAO) with muscular symptoms not included among previous lipid myopathies classifications

    Effects of Auxiliary Knowledge on Continual Learning

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    In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information without forgetting old knowledge (i.e., Catastrophic Forgetting). Most existing CL approaches focus on finding solutions to preserve acquired knowledge, so working on the past of the model. However, we argue that as the model has to continually learn new tasks, it is also important to put focus on the present knowledge that could improve following tasks learning. In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones. More specifically, our approach combines the main data stream with a secondary, diverse and uncorrelated stream, from which the network can draw auxiliary knowledge. This helps the model from different perspectives, since auxiliary data may contain useful features for the current and the next tasks and incoming task classes can be mapped onto auxiliary classes. Furthermore, the addition of data to the current task is implicitly making the classifier more robust as we are forcing the extraction of more discriminative features. Our method can outperform existing state-of-the-art models on the most common CL Image Classification benchmarks

    Muscle MRI in neutral lipid storage disease (NLSD)

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    We present the muscle imaging data of 12 patients from the Italian Registry of Neutral Lipid Storage Disease (NLSD): ten patients presenting NLSD with myopathy (NLSD-M) carrying recessive mutations in PNPLA2 gene, and two patients presenting the NLSD with ichthyosis (NLSD-I) mutated in ABHD5 gene. In NLSD-M gluteus minimus, semimembranosus, soleus andgastrocnemius medialis in the lower limbs and infraspinatus, trapezius, deltoid and paraspinous muscles in the upper limbs were the most affected muscles. Gracilis, sartorius, subscapularis, pectoralis, triceps brachii and sternocleidomastoideus were spared. Muscle involvement was not homogenous and characteristic ‚Äúpatchy‚ÄĚ replacement was observed in at least one muscle in all the patients. Half of the patients showed one or more STIR positive muscles. In both NLSD-I cases muscle involvement was not observed by T1-TSE sequences but one of them showed positive STIR images in more than one muscle in the leg. Our data provide evidence that muscle imaging can identify characteristic alterations in NLSD-M, characterized by a specific pattern of muscle involvement with ‚Äúpatchy‚ÄĚ areas of muscle sparing/replacement. Larger cohorts are needed to assess if a distinct pattern of muscle involvement exists also for NLSD-I
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