Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. Th...
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and *** relevance— To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
Feature subset selection (FSS) for classification is inherently a bi-objective optimization problem, where the task is to obtain a feature subset which yields the maximum possible area under the receiver operator char...
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Feature subset selection (FSS) using a wrapper approach is essentially a combinatorial optimization problem having two objective functions viz., (i) the cardinality of the selected feature subset, and (ii) the a...
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Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and impr...
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This paper presents a comparative analysis of some major High Efficiency Video Coding (HEVC) intra prediction mode decision methods reported in the literature. Intra coding in HEVC is based on spatial sample predictio...
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This paper presents a comparative analysis of some major High Efficiency Video Coding (HEVC) intra prediction mode decision methods reported in the literature. Intra coding in HEVC is based on spatial sample prediction and can be described as a collection of many elements including quad tree partitioning, 35 intra prediction modes, reference sample smoothing, boundary sample filtering process, intra mode coding and so on. Each of these elements results in an improved coding efficiency in HEVC intra coding. In this review, we provide an extensive and up-to-date discussion of the research proposals regarding a variety of HEVC intra prediction mode decision strategies. Moreover, some most significant research contributions on fast intra mode decision methods in HEVC to improvise the encoder efficiency are reviewed in detail along with a comparative performance analysis of the reviewed proposals in terms of some standard evaluation metrics such as bit rate, PSNR and time savings. In addition, this paper point out some research directions towards further developments in the intra prediction for the betterment of the encoder efficacy.
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine ...
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Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysi...
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Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.
Embodied agents in vision navigation coupled with deep neural networks have attracted increasing attention. However, deep neural networks have been shown vulnerable to malicious adversarial noises, which may potential...
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vision Transformers (ViTs) have recently demonstrated remarkable performance in computervision tasks. However, their parameter-intensive nature and reliance on large amounts of data for effective performance have shi...
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Intelligently assessing the quality of athletic performances in sports scenarios remains a fascinating challenge in computervision. However, unraveling the subtle distinctions between two similar actions in videos an...
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