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Genetic as well as environmental benefits to be able to variations on appetitive traits in A decade old enough: any double review inside the Age group XXI beginning cohort.
e., SC) extracted from Diffusion Tensor Imaging (DTI). Based on this graph, we train two Attention-Diffusion-Bilinear (ADB) modules jointly. In each module, an attention model is utilized to automatically learn the strength of node interactions. This information further guides a diffusion process that generates new node representations by considering the influence from other nodes as well. After that, the second-order statistics of these node representations are extracted by bilinear pooling to form connectivity-based features for disease prediction. The two ADB modules correspond to the one-step and two-step diffusion, respectively. Experiments on a real epilepsy dataset demonstrate the effectiveness and advantages of our proposed method.Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of n stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specion method (degrading 25%), (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n=465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.Recent advances in positron emission tomography (PET) have allowed to perform brain scans of freely moving animals by using rigid motion correction. selleck inhibitor One of the current challenges in these scans is that, due to the PET scanner spatially variant point spread function (SVPSF), motion corrected images have a motion dependent blurring since animals can move throughout the entire field of view (FOV). We developed a method to calculate the image-based resolution kernels of the motion dependent and spatially variant PSF (MD-SVPSF) to correct the loss of spatial resolution in motion corrected reconstructions. The resolution kernels are calculated for each voxel by sampling and averaging the SVPSF at all positions in the scanner FOV where the moving object was measured. In resolution phantom scans, the use of the MD-SVPSF resolution model improved the spatial resolution in motion corrected reconstructions and corrected the image deformation caused by the parallax effect consistently for all motion patterns, outperforming the use of a motion independent SVPSF or Gaussian kernels. Compared to motion correction in which the SVPSF is applied independently for every pose, our method performed similarly, but with more than two orders of magnitude faster computation time. Importantly, in scans of freely moving mice, brain regional quantification in motion-free and motion corrected images was better correlated when using the MD-SVPSF in comparison with motion independent SVPSF and a Gaussian kernel. The method developed here allows to obtain consistent spatial resolution and quantification in motion corrected images, independently of the motion pattern of the subject.Public understanding of contemporary scientific issues is critical for the future of society. Public spaces, such as science centers, can impact the communication of science by providing active knowledge-building experiences of scientific phenomena. In contributing to this vision, we have previously developed an interactive visualization as part of a public exhibition about nano. We reflect on how the immersive design and features of the exhibit contribute as a tool for science communication in light of the emerging paradigm of exploranation, and offer some forward-looking perspectives about what this notion has to offer the domain.Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a Semi-supervised Multi-view Deep Discriminant Representation Learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications such as building automation, health monitoring, behavioral intervention and home security. However, when there are multiple residents living in the smart home, the data association between sensor events and residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layout, floor plan and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.OBJECTIVE The ability to measure event-related potentials (ERPs) as practical, portable brain vital signs is limited by the physical locations of electrodes. Standard electrode locations embedded within the hair result in challenges to obtaining quality signals in a rapid manner. Moreover, these sites require electrode gel, which can be inconvenient. As electrical activity in the brain is spatially volume distributed, it should be possible to predict ERPs from distant sensor locations at easily accessible mastoid and forehead scalp regions. METHODS An artificial neural network was trained on ERP signals recorded from below hairline electrode locations (Tp9, Tp10, Af7, Af8 referenced to Fp1, Fp2) to predict signals recorded at the ideal Cz location. RESULTS The model resulted in mean improvements in intraclass correlation coefficient relative to control for all stimulus types (Standard Tones = +9.74%, Deviant Tones = +3.23%, Congruent Words = +15.25%, Incongruent Words = +25.43%) and decreases in RMS Error (Standard Tones = - 26.72%, Deviant Tones = -17.80%, Congruent Words = -28.78%, Incongruent Words = -29.61%) compared to the individual distant channels. Measured vs predicted ERP amplitudes were highly and significantly correlated with control for the N100 (R = 0.5, padj less then 0.05) P300 (R = 0.75, padj less then 0.01), and N400 (R = 0.75, padj less then 0.01) ERPs. CONCLUSION ERP waveforms at distant channels can be combined using a neural network autoencoder to model the control channel features with better precision than those at individual distant channels. This is the first demonstration of feasibility of predicting evoked potentials and brain vital signs using signals recorded from more distant, practical locations. SIGNIFICANCE This solves a key engineering challenge for applications that require portability, comfort, and speed of measurement as design priorities for measurement of event-related potentials across a range of individuals, settings, and circumstances.OBJECTIVE Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition. METHODS We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data. RESULTS VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029mV, yielding a median VFF attenuation of 142.
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