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Quantifying Urinary and Erectile dysfunction Following Pelvic Bone fracture.
We evaluate our glyph's usability through a user study and demonstrate the system's usefulness through a case study with insights approved by coaches and domain experts.Thirty million tendon injuries occur annually in the US costing $114 billion. Conservative therapies, like dry needling, promote healing in chronically injured tendon by inducing microdamage but have mixed success rates. Focused ultrasound (fUS) therapy can noninvasively fractionate tissues through the creation, oscillation, and collapse of bubbles in a process termed histotripsy; however, highly collagenous tissues, like tendon, have shown resistance to mechanical fractionation. This study histologically evaluates whether fUS mechanical disruption is achievable in tendon. Ex vivo rat tendons (45 Achilles and 44 supraspinatus) were exposed to 1.5 MHz fUS operating with 0.1-10 ms pulses repeated at 1-100 Hz for 15-60 s with peak positive pressures less then 89 MPa and peak negative pressures less then 26 MPa; other tendons were exposed to dry needling or sham. Immediately after treatment, tendons were frozen fixed and stained with Hematoxylin and Eosin (H&E) or alpha-nicotinamide adenine dinucleotide diaphorase (α-NADH-d) and evaluated by two reviewers blinded to the exposure conditions. Results showed successful creation of bubbles for all fUS-treated samples; however, not all samples showed histological injury. When injury was detected, parameter sets with shorter pulses (0.1-1 ms), lower acoustic pressures, or reduced treatment times showed mechanical disruption in the form of fiber separation and fraying with little to no thermal injury. Longer pulses or treatment times showed a combination of mechanical and thermal injury. These findings suggest mechanical disruption is achievable in tendon within a small window of acoustic parameters, supporting the potential of fUS therapy in tendon treatment.Angular spectrum (AS) methods enable efficient calculation of wave propagation from one plane to another inside homogeneous media. For wave propagation in heterogeneous media such as biological tissues, AS methods cannot be applied directly. Split-stepping techniques decompose the heterogeneous domain into homogeneous and perturbation parts, and provide a solution for forward wave propagation by propagating the incident wave in both frequency-space and frequency-wavenumber domains. Recently, a split-step Hybrid Angular Spectrum method was proposed for plane wave propagation of focused ultrasound beams. In this study, we extend these methods to enable simulation of acoustic pressure field for an arbitrary source distribution, by decomposing the source and reflection spectra into orthogonal propagation direction components; propagating each component separately and summing all components to get the total field. We show that our method can efficiently simulate the pressure field of arbitrary sources in heterogeneous media. The accuracy of the method was analyzed comparing the resultant pressure field with pseudo-spectral time domain (PSTD) solution for breast tomography and hemispherical transcranial focused ultrasound simulation models. 80 times accelaration was achieved for a 3D breast simulation model compared to PSTD solution with 0.005 normalized root mean squared difference (NRMSD) between two solutions. For the hemispherical phased array, aberrations due to skull were accurately calculated in a single simulation run as evidenced by the resultant focused ultrasound beam simulations, which had 0.001 NRMSD with 40 times acceleration factor compared to the PSTD method.The automatic diagnosis of various conventional ophthalmic diseases from fundus images is important in clinical practice. However, developing such automatic solutions is challenging due to the requirement of a large amount of training data and the expensive annotations for medical images. This paper presents a novel self-supervised learning framework for retinal disease diagnosis to reduce the annotation efforts by learning the visual features from the unlabeled images. To achieve this, we present a rotation-oriented collaborative method that explores rotation-related and rotation-invariant features, which capture discriminative structures from fundus images and also explore the invariant property used for retinal disease classification. this website We evaluate the proposed method on two public benchmark datasets for retinal disease classification. The experimental results demonstrate that our method outperforms other self-supervised feature learning methods (around 4.2% area under the curve (AUC)). With a large amount of unlabeled data available, our method can surpass the supervised baseline for pathologic myopia (PM) and is very close to the supervised baseline for age-related macular degeneration (AMD), showing the potential benefit of our method in clinical practice.This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a '`meta-learner'', '`data ingestor'', '`model selector'', '`model/learner'', and '`evaluator''. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http//autodl.chalearn.org), the open-sourced code of the winners, and a free 'AutoDL self-service''.
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