Callahankenney0439

In addition to the off-the-shelf deep geometry estimation modules, we design an effective fusion module for geometrical information with deep video features. Specifically, similar to model-based optimization, our proposed module recurrently refines video features as well as geometrical information to restore more precise latent frames. To evaluate the effectiveness and generalization of our framework, we perform tests on eight baseline networks whose structures are motivated by the previous research. The experimental results show that our framework offers greater performances than the eight baselines and produces state-of-the-art performance on four video deblurring benchmark datasets.Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a nonlinear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2 -norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2 -, L1 -norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1 -norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography (SOUL) with the L1 -norm spatial regularization ( L1 -SOUL). In terms of both sharpness and visual contrast, L1 -SOUL substantially outperforms GLobal Ultrasound Elastography (GLUE), tOtal Variation rEgulaRization and WINDow-based time delay estimation (OVERWIND), and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1 -SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1 -SOUL code can be downloaded from http//code.sonography.ai.Alternating current poling (ACP) is an effective method to improve the piezoelectric performance of relaxor-PbTiO3 (PT) ferroelectric single crystal. 0.72Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 (PMN-PT) single crystals have been used to fabricate piezoelectric transducers for medical imaging. Up to date, there are no report about the full matrix material constants of PMN-0.28PT single crystals poled by ACP. Here, we report the complete sets of elastic, dielectric, and piezoelectric properties of 001-poled PMN-0.28PT single crystals by direct current poling (DCP) and ACP through the resonance method. The results show that 001-poled rhombohedral PMN-0.28PT single crystals exhibit the enhancement of longitudinal and transverse piezoelectric properties (d33 ~ 2000 pC/N, d31 ~ -1010 pC/N) after ACP. Compared with DCP samples (d33 ~ 1660 pC/N, d31 ~ -780 pC/N), the values of d33 and d31 increase 20% and 29%, respectively. While the d15 value decrease from 110 pC/N for DCP sample to 90 pC/N for ACP sample, showing the decrease in transverse shear piezoelectric properties. In addition, the elastic stiffness coefficient c11, c12, c13, the elastic compliance coefficient s11, s12, and the dielectric constants ε11, ε33 have great change compared with DCP and ACP samples. This variation of the property matrices provides a reference for high-performance piezoelectric device design.The clinical and economic burdens of cardiovascular diseases pose a global challenge. Growing evidence suggests an early assessment of arterial stiffness can provide insights into the pathogenesis of cardiovascular diseases. However, it remains difficult to quantitatively characterize local arterial stiffness in vivo. Here we utilize guided axial waves continuously excited and detected by ultrasound to probe local blood pressures and mechanical properties of common carotid arteries simultaneously. In a pilot study of 17 healthy volunteers, we observe a ∼ 20 % variation in the group velocities of the guided axial waves (5.16 ± 0.55 m/s in systole and 4.31 ± 0.49 m/s in diastole) induced by the variation of the blood pressures. A linear relationship between the square of group velocity and blood pressure is revealed by the experiments and finite element analysis, which enables us to measure the waveform of the blood pressures by the group velocities. Furthermore, we propose a wavelet analysis-based method to extract the dispersion relations of the guided axial waves. We then determined the shear modulus by fitting the dispersion relations in diastole with the leaky Lamb wave model. The average shear modulus of all the volunteers is 166.3 ± 32.8 kPa. No gender differences are found. This study shows the group velocity and dispersion relation of the guided axial waves can be utilized to probe blood pressure and arterial stiffness locally in a noninvasive manner and thus promising for early diagnosis of cardiovascular diseases.Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of individual annotations. We then propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task. We design a dual-uncertainty estimation approach to measure the disagreement label noise and single-target label noise via improved Direct Uncertainty Prediction and Monte-Carlo-Dropout. A boosting-based curriculum training procedure is later introduced for robust learning. We demonstrate the effectiveness of our method by conducting extensive experiments on three different diseases with synthesized and real-world label noise skin lesions, prostate cancer, and retinal diseases. We also release a large re-engineered database that consists of annotations from more than ten ophthalmologists with an unbiased golden standard dataset for evaluation and benchmarking. The dataset is available at https//mmai.group/peoples/julie/.Elastic degree of anisotropy (DoA) is a diagnostically relevant biomarker in muscle, kidney, breast, and other organs. Previously, elastic DoA was qualitatively assessed as the ratio of peak displacements (PD) achieved with the long-axis of a spatially asymmetric Acoustic Radiation Force Impulse (ARFI) excitation point spread function (PSF) aligned along versus across the axis of symmetry (AoS) in transversely isotropic materials. However, to better enable longitudinal and cross-sectional analyses, a quantitative measure of elastic DoA is desirable. In this study, qualitative ARFI PD ratios are converted to quantitative DoA, measured as the ratio of longitudinal over transverse shear elastic moduli, using a model empirically derived from Field II and finite element method (FEM) simulations. In silico, the median absolute percent error (MAPE) in ARFI-derived shear moduli ratio (SMR) was 1.75%, and predicted SMRs were robust to variations in transverse shear modulus, Young's moduli ratio, speed of sound, attenuation, density, and ARFI excitation PSF dimension. Further, ARFI-derived SMRs distinguished two materials when the true SMRs of the compared materials differed by as little as 10%. Experimentally, ARFI-derived SMRs linearly correlated with the corresponding ratios measured by Shear Wave Elasticity Imaging (SWEI) in excised pig skeletal muscle ( [Formula see text], MAPE = 13%) and in pig kidney, in vivo ( [Formula see text], MAPE = 5.3%). click here These results demonstrate the feasibility of using the ARFI PD to quantify elastic DoA in biological tissues.Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel '`base-detector repository and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness against the class-conditional noise. However, they typically focus on the single label case by assuming that only one label is corrupted. In real applications, an instance is usually associated with multiple labels, which could be corrupted simultaneously with their respective conditional probabilities. In this paper, we formalize this problem as a general framework of learning with Class-Conditional Multi-label Noise (CCMN for short). We establish two unbiased estimators with error bounds for solving the CCMN problems, and further prove that they are consistent with commonly used multi-label loss functions. Finally, a new method for partial multi-label learning is implemented with the unbiased estimator under the CCMN framework. Empirical studies on multiple datasets and various evaluation metrics validate the effectiveness of the proposed method.The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the negative sampling strategy to alleviate the time-consuming burden of pairwise computation. However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error. Specifically, we show that the sampling-based CML would introduce a bias term in the generalization bound, which is quantified by the per-user \textitTotal Variance (TV) between the distribution induced by negative sampling and the ground truth distribution. This suggests that optimizing the sampling-based CML loss function does not ensure a small generalization error even with sufficiently large training data. Moreover, we show that the bias term will vanish without the negative sampling strategy. Motivated by this, we propose an efficient alternative without negative sampling for CML named Sampling-Free Collaborative Metric Learning (SFCML), to get rid of the sampling bias in a practical sense.