•Predictor variables determined using LASSO based filtering with majority voting.•Sub sampling methods implemented to account for high class imbalances.•Classifier attains higher class-wise ...accuracies as compared to traditional models.•Proposed architecture can be used as a transfer learning model for new data.
This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data is curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of predicting the occurrence of Coronary Heart Disease (CHD). While the majority of the existing machine learning models that have been used on this class of data are vulnerable to class imbalance even after the adjustment of class-specific weights, our simple two-layer CNN exhibits resilience to the imbalance with fair harmony in class-specific performance. Given a highly imbalanced dataset, it is often challenging to simultaneously achieve a high class 1 (true CHD prediction rate) accuracy along with a high class 0 accuracy, as the test data size increases. We adopt a two-step approach: first, we employ least absolute shrinkage and selection operator (LASSO) based feature weight assessment followed by majority-voting based identification of important features. Next, the important features are homogenized by using a fully connected layer, a crucial step before passing the output of the layer to successive convolutional stages. We also propose a training routine per epoch, akin to a simulated annealing process, to boost the classification accuracy.
Despite a high class imbalance in the NHANES dataset, the investigation confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD and 81.8% to accurately classify the absence of CHD cases on a testing data, which is 85.70% of the total dataset. This result signifies that the proposed architecture can be generalized to other studies in healthcare with a similar order of features and imbalances. While the recall values obtained from other machine learning methods, such as SVM and random forest, are comparable to that of our proposed CNN model, our model predicts the negative (Non-CHD) cases with higher accuracy. Our model architecture exhibits a way forward to develop better investigative tools, improved medical treatment and lower diagnostic costs by incorporating a smart diagnostic system in the healthcare system. The balanced accuracy of our model (79.5%) is also better than individual accuracies of SVM or random forest classifiers. The CNN classifier results in high specificity and test accuracy along with high values of recall and area under the curve (AUC).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence ...to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.
Robust and accurate scale estimation of a target object is a challenging task in visual object tracking. Most existing tracking methods cannot accommodate large scale variation in complex image ...sequences and thus result in inferior performance. In this paper, we propose to incorporate a novel criterion called the average peak-to-correlation energy into the multi-resolution translation filter framework to obtain robust and accurate scale estimation. The resulting system is named SITUP: Scale Invariant Tracking using Average Peak-to-Correlation Energy. SITUP effectively tackles the problem of fixed template size in standard discriminative correlation filter based trackers. Extensive empirical evaluation on the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework meets the demands of scale variation challenges effectively while providing superior performance over other scale adaptive variants of standard discriminative correlation filter based trackers. Also, SITUP obtains favorable performance compared to state-of-the-art trackers for various scenarios while operating in real-time on a single CPU.
This paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention ...model calculates the correlations between spatial and motion features to model spatiotemporal interactions between different action semantics properly. Second, the motion-aware network encodes the locations of action semantics in video frames utilizing the motion-aware 2D positional encoding algorithm. Such a motion-aware mechanism memorizes the dynamic spatiotemporal variations in action frames that current methods cannot exploit. Third, the sequence-based temporal attention model captures the heterogeneous temporal dependencies in action frames. In contrast to standard temporal attention used in natural language processing, primarily aimed at finding similarities between linguistic words, the proposed sequence-based temporal attention is designed to determine both the differences and similarities between video frames that jointly define the meaning of actions. The proposed approach outperforms the state-of-the-art solutions on four spatiotemporal action datasets: AVA 2.2, AVA 2.1, UCF101-24, and EPIC-Kitchens.
In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care ...analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.
•A multi-class skeleton-based gait anomaly recognition framework (SGAR) is presented.•An ablation study illustrates the effect of different elements of the framework.•New data augmentation is ...presented to improve the deep models’ performance for SGAR.•The presented model acquires state-of-the-art performance on three SGAR datasets.
In this work, we propose an end-to-end deep learning model that uses the skeleton data recorded by Kinect to capture spatiotemporal patterns for gait anomaly recognition. Via considering the entire skeleton, the proposed model captures the relationship between different body joints in locomotion. Unlike the common two-class or one-class approaches in skeleton-based methods, the proposed model considers a multi-class framework. Such a multi-class technique can be easily adapted for a more frequent and less expensive gait assessment outside of motion capture facilities. The proposed deep learning model is trained and evaluated on the publicly available Walking gait dataset and achieves an average accuracy of 90.57% in identifying nine different walking patterns. Through transfer learning, we also evaluate our model on two other publicly available datasets, acquiring an average accuracy of 83.64% on a dataset of three classes and 90.83% on a dataset with six classes of normal/pathological gait patterns. The results of this work indicate the potential of markerless modalities such as Kinect for designing less costly and more convenient health infrastructures for assisted living. Additionally, an automatic and non-invasive gait assessment can further augment the clinical diagnosis for an extensive list of ailments that cause different types of gait disorders.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle ...intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using a set of pre-specified basis functions. This representation enables us to accommodate heterogeneous objects, even in presence of noise. We compare our results with three state of the art techniques on a dataset focusing on biological/biomedical images with tubular or filamentous structures. Quantitatively, we achieve a 44% increase in performance, which demonstrates efficacy of the method.
This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for ...speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.
A segmentation framework is proposed to trace neurons from confocal microscopy images. With an increasing demand for high throughput neuronal image analysis, we propose an automated scheme to perform ...segmentation in a variational framework. Our segmentation technique, called tubularity flow field (TuFF) performs directional regional growing guided by the direction of tubularity of the neurites. We further address the problem of sporadic signal variation in confocal microscopy by designing a local attraction force field, which is able to bridge the gaps between local neurite fragments, even in the case of complete signal loss. Segmentation is performed in an integrated fashion by incorporating the directional region growing and the attraction force-based motion in a single framework using level sets. This segmentation is accomplished without manual seed point selection; it is automated. The performance of TuFF is demonstrated over a set of 2D and 3D confocal microscopy images where we report an improvement of >75% in terms of mean absolute error over three extensively used neuron segmentation algorithms. Two novel features of the variational solution, the evolution force and the attraction force, hold promise as contributions that can be employed in a number of image analysis applications.
Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell ...behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.
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