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Precisely detecting solar active regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditional multi-spectral imaging scenarios where all image bands observe the same scene. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR detection where different image bands (and physical locations) each have their own set of results. Different feature fusion strategies are investigated in this work, where information from different image modalities is aggregated at different semantic levels throughout the network. This allows the network to benefit from the joint analysis while preserving the band-specific information. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al. Astron Astrophys 561:16, 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands. We also evaluate our proposed approach on synthetic data of similar spatial configurations obtained from annotated multi-modal magnetic resonance images.

Salient object detection identifies objects in an image that grab visual attention. Although contextual features are considered in recent literature, they often fail in real-world complex scenarios. We observe that this is mainly due to two issues: First, most existing datasets consist of simple foregrounds and backgrounds that hardly represent real-life scenarios. Second, current methods only learn contextual features of salient objects, which are insufficient to model high-level semantics for saliency reasoning in complex scenes. To address these problems, we first construct a new large-scale dataset with complex scenes in this paper. We then propose a context-aware learning approach to explicitly exploit the semantic scene contexts. Specifically, two modules are proposed to achieve the goal: 1) a Semantic Scene Context Refinement module to enhance contextual features learned from salient objects with scene context, and 2) a Contextual Instance Transformer to learn contextual relations between objects and scene context. To our knowledge, such high-level semantic contextual information of image scenes is under-explored for saliency detection in the literature. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art techniques in complex scenarios for saliency detection, and transfers well to other existing datasets. The code and dataset are available at https://github.com/SirisAvishek/Scene_Context_Aware_Saliency.

Accurate prediction of solar panel temperature can help keep on-orbit satellites in good condition. Traditional physical models have the ability to describe and predict temperature; however, the effect is not entirely satisfactory. To produce a better forecast, the panel current of the solar panel, which is strongly correlated with temperature signals, is chosen as the input, and a novel system identification model between the two signals is established. The model we propose is based on a Hammerstein-Wiener model and integrates wavelet neural networks that adopt self-constructed wavelet bases. In addition, a complete process of training and parameter optimization is designed to be less time consuming than previous methods. The result from a test set of real telemetry data demonstrates the efficiency and accuracy of our method. Moreover, the proposed prediction model, which is based on historical data, can be used in on board self-learning and our subsequent autonomous health management.

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

Networks derived from various disciplinary of sociality and nature are dynamic and incomplete, and temporal link prediction has wide applications in recommendation system and data mining system, etc. The current algorithms first obtain features by exploiting the topological or latent structure of networks, and then predict temporal links based on the obtained features. These algorithms are criticized by the separation of feature extraction and link prediction, which fails to fully characterize the dynamics of networks, resulting in undesirable performance. To overcome this problem, we propose a novel algorithm by joint multi-label learning and feature extraction (called MLjFE), where temporal link prediction and feature extraction are integrated into an overall objective function. The main advantage of MLjFE is that the features and parameter matrix for temporal link prediction are simultaneously learned during optimization procedure, which is more precise to capture dynamics of networks, improving the performance of algorithms. The experimental results on a number of artificial and real-world temporal networks demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods, showing joint learning with feature extraction and temporal link prediction is promising.

Segmenting complex 3D geometry is a challenging task due to rich structural details and complex appearance variations of target object. Shape representation and foreground-background delineation are two of the core components of segmentation. Explicit shape models, such as mesh based representations, suffer from poor handling of topological changes. On the other hand, implicit shape models, such as level-set based representations, have limited capacity for interactive manipulation. Fully automatic segmentation for separating foreground objects from background generally utilizes non-interoperable machine learning methods, which heavily rely on the off-line training dataset and are limited to the discrimination power of the chosen model. To address these issues, we propose a novel semi-implicit representation method, namely Non-Uniform Implicit B-spline Surface (NU-IBS), which adaptively distributes parametrically blended patches according to geometrical complexity. Then, a two-stage cascade classifier is introduced to carry out efficient foreground and background delineation, where a simplistic Naive-Bayesian model is trained for fast background elimination, followed by a stronger pseudo-3D Convolutional Neural Network (CNN) multi-scale classifier to precisely identify the foreground objects. A localized interactive and adaptive segmentation scheme is incorporated to boost the delineation accuracy by utilizing the information iteratively gained from user intervention. The segmentation result is obtained via deforming an NU-IBS according to the probabilistic interpretation of delineated regions, which also imposes a homogeneity constrain for individual segments. The proposed method is evaluated on a 3D cardiovascular Computed Tomography Angiography (CTA) image dataset and Brain Tumor Image Segmentation Benchmark 2015 (BraTS2015) 3D Magnetic Resonance Imaging (MRI) dataset.

Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic Encryption (HE), Differential Privacy (DP), etc.) and collaborative training (Secure Multi-Party Computation (MPC), Distributed Learning and Federated Learning (FL)). These techniques have a particular focus on data encryption or secure local computation. They transfer the intermediate information to the third party to compute the final result. Gradient exchanging is commonly considered to be a secure way of training a robust model collaboratively in Deep Learning (DL). However, recent researches have demonstrated that sensitive information can be recovered from the shared gradient. Generative Adversarial Network (GAN), in particular, has shown to be effective in recovering such information. However, GAN based techniques require additional information, such as class labels which are generally unavailable for privacy-preserved learning. In this paper, we show that, in the FL system, image-based privacy data can be easily recovered in full from the shared gradient only via our proposed Generative Regression Neural Network (GRNN). We formulate the attack to be a regression problem and optimize two branches of the generative model by minimizing the distance between gradients. We evaluate our method on several image classification tasks. The results illustrate that our proposed GRNN outperforms state-of-the-art methods with better stability, stronger robustness, and higher accuracy. It also has no convergence requirement to the global FL model. Moreover, we demonstrate information leakage using face re-identification. Some defense strategies are also discussed in this work., The source code can be found at: https://github.com/Rand2AI/GRNN

The net methane emission of any mire ecosystem results from a combination of biological and physical processes, including methane production by archaea, methane consumption by bacteria, and transport of methane from peat to the atmosphere. The complexity of spatial and temporal behavior of methane emission is connected to these.13C-signature of emitted methane offers us a further constraint to evaluate our hypothesis on the processes leading to the variation of methane emission rates. For example, assuming the spatial variation in methane emission rate at microtopographic scale is due to variation in trophic status or variation in methane consumption, will lead to differences in the relation of methane emission rate and its 13C-signature, expressed as δ13C.We have measured the methane emission rates and δ13C of emitted methane by six automated chambers at a poor fen ecosystem over two growing seasons. The measurements were conducted at Mycklemossen mire (58°21'N 12°10'E, 80m a.s.l.), Sweden, during 2019-2020. In addition, we measured atmospheric surface layer methane mixing ratios and δ13C to obtain larger scale 13C-signatures by the nocturnal boundary-layer accumulation (NBL) approach. All δ13C-signatures were derived using the Keeling-plot approach.The collected data shows spatial differences of up to 10-15 ‰ in 10-day averages of δ13C-signatures between different chamber locations. Temporal variations of 10-day average δ13C-signatures from most chamber locations reached over 5 ‰, while the temporal variation of NBL derived δ13C-signature was slightly lower.The observed spatial variation in the δ13C-signature was somewhat systematic, indicating, especially in the middle of the summers, the main control of spatial variation of methane emission to be the trophic status. The temporal changes, measured at different locations, indicate spatial differences in the temporal dynamics at the microtopographic scale. The temporal behavior of larger scale NBL δ13C-signature does not fully correspond to the behavior of the chamber derived average δ13C-signature.

International audience; Precisely detecting solar Active Regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditional multi-spectral imaging scenarios where all image bands observe the same scene. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR detection where different image bands (and physical locations) each have their own set of results. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al., 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands.

Managing and treating cardiovascular diseases can be substantially improved by automatic detection and classification of the heart arrhythmia. In this paper, we introduced a novel deep learning system for classifying the electrocardiogram (ECG) signals. The heartbeats are classified into different arrhythmia types using two proposed deep learning models. The first model is integrating the convolutional neural network (CNN) and long short-term memory (LSTM) network to extract useful features within the ECG signal. The second model combines several classical features with LSTM in order to effectively recognize abnormal classes. These deep learning models are trained using a bagging model then aggregated by a fusion classifier to form a robust unified model. The proposed system is evaluated on the MIT-BIH arrhythmia database and produces an overall accuracy of 95.81%, which significantly outperforms the state-of-the-art.

There has been growing interest recently in using machine learning techniques as an aid in clinical medicine. Machine learning offers a range of classification algorithms which can be applied to medical data to aid in making clinical predictions. Recent studies have demonstrated the high predictive accuracy of various classification algorithms applied to clinical data. Several studies have already been conducted in diagnosing or predicting chronic kidney disease at various stages using different sets of variables. In this study we are investigating the use of machine learning techniques with blood test data. Such a system could aid renal teams in making recommendations to primary care general practitioners to refer patients to secondary care where patients may benefit from earlier specialist assessment and medical intervention. We are able to achieve an overall accuracy of 88.48% using logistic regression, 87.12% using ANN and 85.29% using SVM. ANNs performed with the highest sensitivity at 89.74 % compared to 86.67 % for logistic regression and 85.51 % for SVM.

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.

Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings.

Sepsis presents a significant challenge to healthcare providers during critical care scenarios such as within an intensive care unit. The prognosis of the onset of severe septic shock results in significant increases in mortality rate, length of stay and readmission rates. Continual advancements in health informatics data allows for applications within the machine learning field to predict sepsis onset in a timely manner, allowing for effective preventative intervention of severe septic shock. A novel deep learning application is proposed to provide effective prediction of sepsis onset by up to six hours prior, involving the use of novel concepts such as a boosted cascading training methodology and adjustable margin hinge loss function. The proposed methodology provides statistically significant improvements to that of current machine learning based modelling applications based off the Physionet Computing in Cardiology 2019 challenge. Results show test F1 scores of 0.420, a significant improvement of 0.281 as compared to the next best challenger results.

Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs.

The achievement of neural networks in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose an efficient method to simultaneously identify the critical neurons and prune the model during training without involving any pre-training or fine-tuning procedures. Unlike existing methods, which accomplish this task in a greedy fashion, we propose a majority voting technique to compare the activation values among neurons and assign a voting score to quantitatively evaluate their importance. This mechanism helps to effectively reduce model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Experimental results show that majority voting efficiently compresses the network with no drop in model accuracy, pruning more than 79% of the original model parameters on CIFAR10 and more than 91% of the original parameters on MNIST. Moreover, we show that with our proposed method, sparse models can be further pruned into even smaller models by removing more than 60% of the parameters, whilst preserving the reference model accuracy.

Psychology studies and behavioural observation show that humans shift their attention from one location to another when viewing an image of a complex scene. This is due to the limited capacity of the human visual system in simultaneously processing multiple visual inputs. The sequential shifting of attention on objects in a non-task oriented viewing can be seen as a form of saliency ranking. Although there are methods proposed for predicting saliency rank, they are not able to model this human attention shift well, as they are primarily based on ranking saliency values from binary prediction. Following psychological studies, in this paper, we propose to predict the saliency rank by inferring human attention shift. Due to the lack of such data, we first construct a large-scale salient object ranking dataset. The saliency rank of objects is defined by the order that an observer attends to these objects based on attention shift. The final saliency rank is an average across the saliency ranks of multiple observers. We then propose a learning-based CNN to leverage both bottom-up and top-down attention mechanisms to predict the saliency rank. Experimental results show that the proposed network achieves state-of-the-art performances on salient object rank prediction. Code and dataset are available at https://github.com/SirisAvishek/Attention_Shift_Ranks

The achievement of convolutional neural networks (CNNs) in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts to evaluate the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a semantic concept and individual hidden unit representations is proposed to quantitatively evaluate the importance of feature maps. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining, crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines. Furthermore, we evaluate our pruning method on CIFAR-10, CUB-200, and ImageNet (ILSVRC 2012) datasets. The experimental results show that the proposed method efficiently achieves a 50% FLOPs reduction on CIFAR-10, with only 0.86% accuracy drop on the VGG-16 model. Meanwhile, ResNet pruned on CIFAR-10 achieves a 30% reduction in FLOPs with only 0.12% and 0.02% drops in accuracy on ResNet-20 and ResNet-32 respectively. For ResNet-50 on ImageNet, our pruned model achieves a 50% reduction in FLOPs with only a top-5 accuracy drop of 0.27%, which significantly outperforms state-of-the-art methods.

This study proposes a decentralized sliding-mode and fault-tolerant control design for a kind of heterogeneous multiagent systems which contains matched disturbances, actuator faults and nonlinear interactions. The sliding-mode function and the decentralized fault-tolerant control protocol are developed so that the closed-loop systems can be asymptotically stable and the matched disturbances and actuator faults can be solved by the adaptive estimated upper bounding law. Simulation results validate the effectiveness of the proposed decentralized sliding-mode fault-tolerant control algorithm.

In this paper, we present a novel image segmentation technique, based on hidden Markov model (HMM), which we then apply to simultaneously segment interior and exterior walls of fluorescent confocal images of lymphatic vessels. Our proposed method achieves this by tracking hidden states, which are used to indicate the locations of both the inner and outer wall borders throughout the sequence of images. We parameterize these vessel borders using radial basis functions (RBFs), thus enabling us to minimize the number of points we need to track as we progress through multiple layers and therefore reduce computational complexity. Information about each border is detected using patch-wise convolutional neural networks (CNN). We use the softmax function to infer the emission probability and use a proposed new training algorithm based on s-excess optimization to learn the transition probability. We also introduce a new optimization method to determine the optimum sequence of the hidden states. Thus, we transform the segmentation problem into one that minimizes an s-excess graph cut, where each hidden state is represented as a graph node and the weight of these nodes are defined by their emission probabilities. The transition probabilities are used to define relationships between neighboring nodes in the constructed graph. We compare our proposed method to the Viterbi and Baum-Welch algorithms. Both qualitative and quantitative analysis show superior performance of the proposed methods.

Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.

Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lower-dimensional representation and find idealistic representative centers for distributed data. In this paper, we present a deep clustering model carried out in presence of varying degrees of supervision. We propose a new version of DCAE to include a supervision component. It introduces a mechanism to inject various levels of supervision into the learning process. This mechanism helps to effectively reconcile extracted latent representations and provided supervising knowledge in order to produce the best discriminative attributes. The key idea of our approach is distinguishing the discriminatory power of numerous structures, through varying degrees of supervision, when searching for a compact structure to form robust clusters. We evaluate our model on MNIST, USPS, MNIST fashion, SVHN datasets and show clustering accuracy on different supervisory levels.

3D human pose estimation is a very difficult task. In this paper we propose that this problem can be more easily solved by first finding the solutions to a set of easier sub-problems. These are to locally estimate pose conditioned on a fixed root node state, which defines the global position and orientation of the person. The global solution can then be found using information extracted during this procedure. This approach has two key benefits: The first is that each local solution can be found by modeling the articulated object as a kinematic chain, which has far less degrees of freedom than alternative models. The second is that by using this approach we can represent, or support, a much larger area of the posterior than is currently possible. This allows far more robust algorithms to be implemented since there is far less pressure to prune the search space to free up computational resources. We apply this approach to two problems: The first is single frame monocular 3D pose estimation, where we propose a method to directly extract 3D pose without first extracting any intermediate 2D representation or being dependent on strong spatial prior models. The second is multi-view 3D tracking where we show that using the above technique results in an approach that is far more robust than current approaches, without relying on strong temporal prior models. In both domains we demonstrate the strength and versatility of the proposed method. A new approach to searching global solution by first finding a set of local solutionsApplied to both monocular 3D pose detection and multiview 3D trackingAchieve good accuracy using weak likelihood model and no assumed motion modelSource code is publically available to ensure reproducible results.

The geometric potential force (GPF) used in segmentation of medical images is in general a robust method. However, calculation of the GPF is often time consuming and slow. In the present work, we propose several methods for improving the GPF calculation and evaluate their efficiency against the original method. Among different methods investigated, the procedure that combines Riesz transform and integration by part provides the fastest solution. Both static and dynamic images have been employed to demonstrate the efficacy of the proposed methods.

Automatic deformable 3D modeling is computationally expensive, especially when considering complex position, orientation and scale variations. We present a volume segmentation framework to utilize local and global regularizations in a data-driven approach. We introduce automated correspondence search to avoid manually labeling landmarks and improve scalability. We propose a novel marginal space learning technique, utilizing multi-resolution pooling to obtain local and contextual features without training numerous detectors or excessively dense patches. Unlike conventional convolutional neural network operators, graph-based operators allow spatially related features to be learned on the irregular domain of the multi-resolution space, and a graph-based convolutional neural network is proposed to learn representations for position and orientation classification. The graph-CNN classifiers are used within a marginal space learning framework to provide efficient and accurate shape pose parameter hypothesis prediction. During segmentation, a global constraint is initially non-iteratively applied, with local and geometric constraints applied iteratively for refinement. Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans. The proposed method shows improvement in both pose parameter estimation and segmentation performance.

High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such information is the semantic segmentation of a shape into functional or meaningful parts. Generating accurate segmentations with meaningful segment boundaries is, however, a costly process, typically requiring large amounts of user time to achieve high quality results. In this paper we present an active learning framework for large dataset segmentation, which iteratively provides the user with new predictions by training new models based on already segmented shapes. Our proposed pipeline consists of three novel components. First, we a propose a fast and relatively accurate feature-based deep learning model to provide dataset-wide segmentation predictions. Second, we propose an information theory measure to estimate the prediction quality and for ordering subsequent fast and meaningful shape selection. Our experiments show that such suggestive ordering helps reduce users time and effort, produce high quality predictions, and construct a model that generalizes well. Finally, we provide effective segmentation refinement features to help the user quickly correct any incorrect predictions. We show that our framework is more accurate and in general more efficient than state-of-the-art, for massive dataset segmentation with while also providing consistent segment boundaries., 16 pages, 17 figures, 5 tables

In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The experimental results show that the proposed method extracts more discriminative features than the features learnt through CNN only. It also suggests the feasibility of classifying different AMD stages using the textural information of the choroid region.

In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality.

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area., This paper provides a comprehensive review for researchers of computer vision techniques and methodologies in transcatheter intervention.

Summary Image-based non-invasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFRro) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRro was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR3D). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R = 0.885, P < 0.01), when using cFFR3D as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFRro and cFFR3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses. This article is protected by copyright. All rights reserved.

Face detection in the wild is a challenging vision problem due to large variations and unpredictable ambiguities commonly existed in real world images. Whilst introducing powerful but complex models is often computationally inefficient, using hand-crafted features is hence problematic. In this paper, we propose a nested CNN-cascade learning algorithm that adopts shallow neural network architectures that allow efficient and progressive elimination of negative hypothesis from easy to hard via self-learning discriminative representations from coarse to fine scales. The face detection problem is considered as solving three sub-problems: eliminating easy background with a simple but fast model, then localising the face region with a soft-cascade, followed by precise detection and localisation by verifying retained regions with a deeper and stronger model. The face detector is trained on the AFLW dataset following the standard evaluation procedure, and the method is tested on four other public datasets, i.e. FDDB, AFW, CMU-MIT and GENKI. Both quantitative and qualitative results on FDDB and AFW are reported, which show promising performances on detecting faces in unconstrained environment.

The field of Human Action Recognition has expanded greatly in previous years, exploring actions and interactions between individuals via the use of appearance and depth based pose information. There are numerous datasets that display action classes composed of behaviors that are well defined by their key poses, such as ‘kicking’ and ‘punching’. The CONVERSE dataset presents conversational interaction classes that show little explicit relation to the poses and gestures they exhibit. Such a complex and subtle set of interactions is a novel challenge to the Human Action Recognition community, and one that will push the cutting edge of the field in both machine learning and the understanding of human actions. CONVERSE contains recordings of two person interactions from 7 conversational scenarios, represented as sequences of human skeletal poses captured by the Kinect depth sensor. In this study we discuss a method providing ground truth labelling for the set, and the complexity that comes with defining such annotation. The CONVERSE dataset it made available online.

A novel fully automated graph cut method to segment IVUS and OCT images.Shape priors to impose global and local shape constraints.Adaptive cost function based on data driven artifact classification.Tracking integrated into graph segmentation to efficiently impose temporal consistency. We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels. The proposed approach is applied to IVUS and OCT images. Both qualitative and quantitative results show superior performance of the proposed method compared to a number of alternative segmentation techniques.