Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is\na challenging task due to the uneven, irregular and unstructured size and shape of tumors.\nRecently, brain tumor segmentation methods based on the symmetric U-Net architecture have\nachieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for\nfeature extraction and restoration has also been shown in recent works, which may encourage\nthe better performance of the brain tumor segmentation problem. Inspired by this, we try to\nintroduce the attention mechanism into the existing U-Net architecture to explore the effects of\nlocal important responses on this task. More specifically, we propose an end-to-end 2D brain\ntumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously\nembeds attention mechanism and residual units into U-Net for the further performance improvement\nof brain tumor segmentation. AResU-Net adds a series of attention units among corresponding\ndown-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance\nlocal responses of down-sampling residual features utilized for the feature recovery of the following\nup-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation\nbenchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed\nAResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor\nsegmentation methods....
(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis.\nWhile novel therapies improve outcomes, many affected individuals remain undiagnosed due to a\nlack of awareness among clinicians. This study was undertaken to develop an expert-independent\nmachine learning (ML) prediction model for CA relying on routinely determined laboratory parameters.\n(2) Methods: In a first step, we developed baseline linear models based on logistic regression.\nIn a second step, we used an ML algorithm based on gradient tree boosting to improve our linear\nprediction model, and to perform non-linear prediction. Then, we compared the performance of\nall diagnostic algorithms. All prediction models were developed on a training cohort, consisting\nof patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF)\npatients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate\nprognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best\nmodel, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver\noperating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2%\nand 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and\nspecificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it\npossible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared\nwith CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the\ndiagnostic workup of CA and may assist physicians in clinical reasoning....
The virtual (software) instrument with a statistical analyzer for testing algorithms\nfor biomedical signalsâ?? recovery in compressive sensing (CS) scenario is presented. Various CS\nreconstruction algorithms are implemented with the aim to be applicable for different types of\nbiomedical signals and different applications with under-sampled data. Incomplete sampling/sensing\ncan be considered as a sort of signal damage, where missing data can occur as a result of noise or\nthe incomplete signal acquisition procedure. Many approaches for recovering the missing signal\nparts have been developed, depending on the signal nature. Here, several approaches and their\napplications are presented for medical signals and images. The possibility to analyze results using\ndifferent statistical parameters is provided, with the aim to choose the most suitable approach\nfor a specific application. The instrument provides manifold possibilities such as fitting different\nparameters for the considered signal and testing the efficiency under different percentages of missing\ndata. The reconstruction accuracy is measured by the mean square error (MSE) between original\nand reconstructed signal. Computational time is important from the aspect of power requirements,\nthus enabling the selection of a suitable algorithm. The instrument contains its own signal database,\nbut there is also the possibility to load any external data for analysis....
As a bridge from the sound signal in the air to the sound perception of the inner ear auditory receptor, the tympanic membrane and\nossicular chain of the middle ear transform the sound signal in the outer ear through two gas-solid and solid-liquid conversions. In\naddition, through the lever principle formed by three auditory ossicle structure, the sound was concentrated and amplified to the\ninner ear. However, the sound transmission function of the middle ear will be decreased by disease, genetic, or trauma. Hence, using\nmiddle ear prosthesis to replace the damaged ossicles can restore the conduction function. The function realization of middle ear\nprosthesis depends on the vibration response of the prosthesis from the tympanic membrane to the stapes plate on the human\nauditory perception frequency, which is affected by the way the prosthesis combined with the tympanic membrane, the material,\nand the geometric shape. In this study, reasonable prosthetic structures had been designed for different types of ossicular chain\ninjuries, and the frequency response characteristics were analyzed by the finite element method then. Moreover, in order to\nachieve better vibration frequency response, a ball structure was designed in the prosthesis to simulate its amplification function.\nThe results showed that the middle ear prostheses constructed by different injury types can effectively transfer vibration energy.\nIn particular, the first- and second-order resonant frequencies and response amplitudes are close to each other when ball\nstructure models of different materials are added. Instead, the resonance frequency of the third stage formed by aluminum alloy\nball materials is larger than that of the other two, which showed good response features....
The aim of this study was to examine whether there are kinematic and kinetic differences\nin the lower limb and whether the symmetry of the lower extremities is different after prolongedrunning.\nFifteen healthy male amateur runners............
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