1,721,181 research outputs found
Geometric Calculus Applications to Medical Imaging: Status and Perspectives
No full textMedical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined
A novel solution based on scale invariant feature transform descriptors and deep learning for the detection of suspicious regions in mammogram images.
Full text linkBackground: Deep learning methods have become popular for their high‑performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. Methods: We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine‑tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT‑based method, while the deep learning network validates the candidate suspicious regions detected by the SIFT method. Results: The experiments conducted on both mini‑MIAS dataset and our new public dataset Suspicious Region Detection on Mammogram from PP (SuReMaPP) of 384 digital mammograms exhibit high performances compared to several state‑of‑the‑art methods. Our solution reaches 98% of sensitivity and 90% of specificity on SuReMaPP and 94% of sensitivity and 91% of specificity on mini‑MIAS. Conclusions: The experimental sessions conducted so far prompt us to further investigate the powerfulness of transfer learning over different CNNs and possible combinations with unsupervised techniques. Transfer learning performances’ accuracy may decrease when the training and testing images come out from mammography devices with different properties
An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm
Full text linkConformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78x and 246x for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor
Design exploration of aes accelerators on FPGAS and GPUs
No full textThe embedded systems are increasingly becoming a key technological component of all kinds of complex tech-nical systems and an exhaustive analysis of the state of the art of all current performance with respect to architectures, design methodologies, test and applications could be very in-teresting. The Advanced Encryption Standard (AES), based on the well-known algorithm Rijndael, is designed to be easily implemented in hardware and software platforms. General purpose computing on graphics processing unit (GPGPU) is an alternative to recongurable accelerators based on FPGA devices. This paper presents a direct comparison between FPGA and GPU used as accelerators for the AES cipher. The results achieved on both platforms and their analysis has been compared to several others in order to establish which device is best at playing the role of hardware accel-erator by each solution showing interesting considerations in terms of throughput, speedup factor, and resource usage. This analysis suggests that, while hardware design on FPGA remains the natural choice for consumer-product design, GPUS are nowadays the preferable choice for PC based ac-celerators, especially when the processing routines are highly parallelizable
Bio-inspired security analysis for IoT scenarios
Full text linkComputer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory to some IoT scenarios as use cases are also outlined
Fingerprint classification based on deep learning approaches: Experimental findings and comparisons
Full text linkBiometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed
A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks
No full textService-continuity in distributed computing can be enhanced by designing self-organized systems, with a non-fixed structure, able to modify their structure and organization, as well as adaptively react to internal and external environment changes. In this paper, an architecture exploiting a bio-inspired management approach, i.e., the functioning of cell metabolism, for specialized computing environments in Service-Oriented Networks (SONs) is proposed. Similar to the processes acting in metabolic networks, the nodes communicate to each other by means of stimulation or suppression chains giving rise to emergent behaviors to defend against foreign invaders, attacks, and malfunctioning. The main contribution of this work is a novel bio-inspired methodology for SON analysis to improve the network reliability and robustness for maintaining service-continuity. To show the effectiveness of the proposed computational framework, an embedded Field-Programmable Gate Array (FPGA) prototyped SON for a relevant healthcare imaging application is also outlined. In particular, our case study extracts and analyzes the Cerebral Vascular Tree from Magnetic Resonance Angiography series via a Maximum Intensity Projection algorithm; the proposed solution addresses and implements some basic issues of an interesting diagnosis tool for cerebral aneurysm detection. The prototyped system was tested and evaluated in terms of execution time and used resource analysis, by achieving a 4× speed-up factor compared to the software counterpart
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine
Full text linkThe ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians’ cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review
A framework for data-driven adaptive GUI generation based on DICOM
No full textComputer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investigation following a well-defined workflow. This paper proposes a software framework aimed at addressing the issues outlined before. Specifically, we designed a DICOM based mechanism of data-driven GUI generation, referring to the examined body part and imaging modality as well as to the medical image analysis task to perform. In this way, the self-configuring GUI is generated on-the-fly, so that just specific functionalities are active according to the current clinical scenario. Such a solution provides also a tight integration with the DICOM standard, which considers various aspects of the technology in medicine but does not address GUI specification issues. The proposed workflow is designed for diagnostic workstations with a local file system on an interchange media acting inside or outside the hospital ward. Accordingly, the DICOMDIR conceptual data model, defined by a hierarchical structure, is exploited and extended to include the GUI information thanks to a new Information Object Module (IOM), which reuses the DICOM information model. The proposed framework exploits the DICOM standard representing an enabling technology for an auto-consistent solution in medical diagnostic applications. In this paper we present a detailed description of the framework, its software design, and a proof-of-concept implementation as a suitable plug-in of the OsiriX imaging software
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