Segmentation of common and internal carotid arteries from 3D ultrasound images using adaptive triple U-Net

Objective: Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT) measured from 3D ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of automating only CCA segmentation. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Methods: Segmentation for CCA and ICA were performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL). A test-time augmentation (TTA) approach is used, in which segmentation was performed three times based on axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting. Results: Experiments involving 224 3DUS volumes produce a Dice-similarity-coefficient (DSC) of 95.1%$\pm$4.1% and 91.6%$\pm$6.6% for the MAB and LIB, in the CCA, respectively, and 94.2%$\pm$3.3% and 89.0%$\pm$8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. The total time required to segment the entire 3DUS volume (CCA+ICA) is 1.4s. Conclusion: The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Significance: Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

Multidimensional Information Assisted Deep Learning Realizing Flexible Recognition of Vortex Beam Modes

Because of the unlimited range of state space, orbital angular momentum (OAM) as a new degree of freedom of light has attracted great attention in optical communication field. Recently there are a number of researches applying deep learning on recognition of OAM modes through atmospheric turbulence. However, there are several limitations in previous deep learning recognition methods. They all require a constant distance between the laser and receiver, which makes them clumsy and not practical. As far as we know, previous deep learning methods cannot sort vortex beams with positive and negative topological charges, which can reduce information capacity. A Multidimensional Information Assisted Deep Learning Flexible Recognition (MIADLFR) method is proposed in this letter. In MIADLR we utilize not only the intensity profile, also spectrum information to recognize OAM modes unlimited by distance and sign of topological charge (TC). As far as we know, we first make use of multidimensional information to recognize OAM modes and we first utilize spectrum information to recognize OAM modes. Recognition of OAM modes unlimited by distance and sign of TC achieved by MIADLFR method can make optical communication and detection by OAM light much more attractive.

$P^2$ Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation

We propose an augmented Parallel-Pyramid Net ($P^2~Net$) with feature refinement by dilated bottleneck and attention module. During data preprocessing, we proposed a differentiable auto data augmentation ($DA^2$) method. We formulate the problem of searching data augmentaion policy in a differentiable form, so that the optimal policy setting can be easily updated by back propagation during training. $DA^2$ improves the training efficiency. A parallel-pyramid structure is followed to compensate the information loss introduced by the network. We innovate two fusion structures, i.e. Parallel Fusion and Progressive Fusion, to process pyramid features from backbone network. Both fusion structures leverage the advantages of spatial information affluence at high resolution and semantic comprehension at low resolution effectively. We propose a refinement stage for the pyramid features to further boost the accuracy of our network. By introducing dilated bottleneck and attention module, we increase the receptive field for the features with limited complexity and tune the importance to different feature channels. To further refine the feature maps after completion of feature extraction stage, an Attention Module ($AM$) is defined to extract weighted features from different scale feature maps generated by the parallel-pyramid structure. Compared with the traditional up-sampling refining, $AM$ can better capture the relationship between channels. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the best performance on the challenging MSCOCO and MPII datasets.

Rescuing neural spike train models from bad MLE

The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction. This maximum likelihood estimation (MLE) often leads to models that perform poorly when generating samples recursively for more than one time step. Moreover, the generated spike trains can fail to capture important features of the data and even show diverging firing rates. To alleviate this, we propose to directly minimize the divergence between neural recorded and model generated spike trains using spike train kernels. We develop a method that stochastically optimizes the maximum mean discrepancy induced by the kernel. Experiments performed on both real and synthetic neural data validate the proposed approach, showing that it leads to well-behaving models. Using different combinations of spike train kernels, we show that we can control the trade-off between different features which is critical for dealing with model-mismatch.

Non-parametric generalized linear model

A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to fit the spike trains with an autoregressive point process model. These models are characterized by a set of convolutional temporal filters, whose subsequent analysis can help reveal how neurons encode stimuli, interact with each other, and process information. In practice a sufficiently rich but small ensemble of temporal basis functions needs to be chosen to parameterize the filters. However, obtaining a satisfactory fit often requires burdensome model selection and fine tuning the form of the basis functions and their temporal span. In this paper we propose a nonparametric approach for jointly inferring the filters and hyperparameters using the Gaussian process framework. Our method is computationally efficient taking advantage of the sparse variational approximation while being flexible and rich enough to characterize arbitrary filters in continuous time lag. Moreover, our method automatically learns the temporal span of the filter. For the particular application in neuroscience, we designed priors for stimulus and history filters useful for the spike trains. We compare and validate our method on simulated and real neural spike train data.

Machine Learning in Population and Public Health

Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole. We present here a very brief introduction into research in these fields, as well as connections to existing machine learning work to help activate the machine learning community on such topics and highlight specific opportunities where machine learning, public and population health may synergize to better achieve health equity.

Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation

The target of human pose estimation is to determine body part or joint locations of each person from an image. This is a challenging problems with wide applications. To address this issue, this paper proposes an augmented parallel-pyramid net with attention partial module and differentiable auto-data augmentation. Technically, a parallel pyramid structure is proposed to compensate the loss of information. We take the design of parallel structure for reverse compensation. Meanwhile, the overall computational complexity does not increase. We further define an Attention Partial Module (APM) operator to extract weighted features from different scale feature maps generated by the parallel pyramid structure. Compared with refining through upsampling operator, APM can better capture the relationship between channels. At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy. We define a new pose search space where the sequences of data augmentations are formulated as a trainable and operational CNN component. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the top-1 accuracy on the challenging COCO keypoint benchmark and the state-of-the-art results on the MPII datasets.

On the Role of Dataset Quality and Heterogeneity in Model Confidence

Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident predictions, it is unclear how model confidence is impacted by the variations in the data, such as label noise or class size. In this paper, we investigate the role of the dataset quality by studying the impact of dataset size and the label noise on the model confidence. We theoretically explain and experimentally demonstrate that, surprisingly, label noise in the training data leads to under-confident networks, while reduced dataset size leads to over-confident models. We then study the impact of dataset heterogeneity, where data quality varies across classes, on model confidence. We demonstrate that this leads to heterogenous confidence/accuracy behavior in the test data and is poorly handled by the standard calibration algorithms. To overcome this, we propose an intuitive heterogenous calibration technique and show that the proposed approach leads to improved calibration metrics (both average and worst-case errors) on the CIFAR datasets.

Streaming Variational Monte Carlo

Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneously inferring the state and their nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides a filtering posterior arbitrarily close to the true filtering distribution for a wide class of dynamics models and observation models. Specifically, the proposed framework can efficiently infer a posterior over the dynamics using sparse Gaussian processes. Constant time complexity per sample makes our approach amenable to online learning scenarios and suitable for real-time applications.