Title: Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks
Individuals who are blind adopt multiple procedures
to tactually explore images. Automatically recognizing and classifying
users’ exploration behaviors is the first step towards the
development of an intelligent system that could assist users to
explore images more efficiently. In this paper, a computational
framework was developed to classify different procedures used
by blind users during image exploration. Translation-, rotationand
scale-invariant features were extracted from the trajectories
of users movements. These features were divided as numerical
and logical features and were fed into neural networks. More
specifically, we trained spiking neural networks (SNNs) to further
encode the numerical features as model strings. The proposed
framework employed a distance-based classification scheme to
determine the final class/label of the exploratory procedures.
Dempster-Shafter Theory (DST) was applied to integrate the
distances obtained from all the features. Through the experiments
of different dynamics of spiking neurons, the proposed framework
achieved a good performance with 95.89% classification
accuracy. It is extremely effective in encoding and classifying
spatio-temporal data, as compared to Dynamic Time Warping
and Hidden Markov Model with 61.30% and 28.70% accuracy.
The proposed framework serves as the fundamental block for
the development of intelligent interfaces, enhancing the image
exploration experience for the blind. more »« less
Zelinski, Michael E.; Taha, Tarek M.; Howe, Jonathan
(Ed.)
Image classification forms an important class of problems in machine learning and is widely used in many realworld applications, such as medicine, ecology, astronomy, and defense. Convolutional neural networks (CNNs) are machine learning techniques designed for inputs with grid structures, e.g., images, whose features are spatially correlated. As such, CNNs have been demonstrated to be highly effective approaches for many image classification problems and have consistently outperformed other approaches in many image classification and object detection competitions. A particular challenge involved in using machine learning for classifying images is measurement data loss in the form of missing pixels, which occurs in settings where scene occlusions are present or where the photodetectors in the imaging system are partially damaged. In such cases, the performance of CNN models tends to deteriorate or becomes unreliable even when the perturbations to the input image are small. In this work, we investigate techniques for improving the performance of CNN models for image classification with missing data. In particular, we explore training on a variety of data alterations that mimic data loss for producing more robust classifiers. By optimizing the categorical cross-entropy loss function, we demonstrate through numerical experiments on the MNIST dataset that training with these synthetic alterations can enhance the classification accuracy of our CNN models.
Wang, Meng-Xiang; Lee, Wang-Chien; Fu, Tao-Yang; Yu, Ge(
, ACM Transactions on Intelligent Systems and Technology)
Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework, we propose a new neural network model, namely Road Network to Vector (RN2Vec), to learn embeddings of intersections and road segments jointly by exploring geo-locality and homogeneity of them, topological structure of the road networks, and moving behaviors of road users. In addition to model design, issues involving data preparation for model training are examined. We evaluate the learned embeddings via extensive experiments on several real-world datasets using different downstream test cases, including node/edge classification and travel time estimation. Experimental results show that the proposed RN2Vec robustly outperforms existing methods, including (i) Feature-based methods : raw features and principal components analysis (PCA); (ii) Network embedding methods : DeepWalk, LINE, and Node2vec; and (iii) Features + Network structure-based methods : network embeddings and PCA, graph convolutional networks, and graph attention networks. RN2Vec significantly outperforms all of them in terms of F1-score in classifying traffic signals (11.96% to 16.86%) and crossings (11.36% to 16.67%) on intersections and in classifying avenue (10.56% to 15.43%) and street (11.54% to 16.07%) on road segments, as well as in terms of Mean Absolute Error in travel time estimation (17.01% to 23.58%).
Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.
Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCs under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neural networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding.
Jin, Geonsoo; Hong, Seongwoo; Rich, Joseph; Xia, Jianping; Kim, Kyeri; You, Lingchong; Zhao, Chenglong; Huang, Tony Jun(
, Lab on a Chip)
Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1000 images of training sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50 000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles.
Zhang, Ting, Duerstock, Bradley S., and Wachs, Juan P. Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks. Retrieved from https://par.nsf.gov/biblio/10142761. IEEE Transactions on Neural Systems and Rehabilitation Engineering NA.NA Web. doi:10.1109/TNSRE.2019.2959555.
Zhang, Ting, Duerstock, Bradley S., & Wachs, Juan P. Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, NA (NA). Retrieved from https://par.nsf.gov/biblio/10142761. https://doi.org/10.1109/TNSRE.2019.2959555
Zhang, Ting, Duerstock, Bradley S., and Wachs, Juan P.
"Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks". IEEE Transactions on Neural Systems and Rehabilitation Engineering NA (NA). Country unknown/Code not available. https://doi.org/10.1109/TNSRE.2019.2959555.https://par.nsf.gov/biblio/10142761.
@article{osti_10142761,
place = {Country unknown/Code not available},
title = {Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks},
url = {https://par.nsf.gov/biblio/10142761},
DOI = {10.1109/TNSRE.2019.2959555},
abstractNote = {Individuals who are blind adopt multiple procedures to tactually explore images. Automatically recognizing and classifying users’ exploration behaviors is the first step towards the development of an intelligent system that could assist users to explore images more efficiently. In this paper, a computational framework was developed to classify different procedures used by blind users during image exploration. Translation-, rotationand scale-invariant features were extracted from the trajectories of users movements. These features were divided as numerical and logical features and were fed into neural networks. More specifically, we trained spiking neural networks (SNNs) to further encode the numerical features as model strings. The proposed framework employed a distance-based classification scheme to determine the final class/label of the exploratory procedures. Dempster-Shafter Theory (DST) was applied to integrate the distances obtained from all the features. Through the experiments of different dynamics of spiking neurons, the proposed framework achieved a good performance with 95.89% classification accuracy. It is extremely effective in encoding and classifying spatio-temporal data, as compared to Dynamic Time Warping and Hidden Markov Model with 61.30% and 28.70% accuracy. The proposed framework serves as the fundamental block for the development of intelligent interfaces, enhancing the image exploration experience for the blind.},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
volume = {NA},
number = {NA},
author = {Zhang, Ting and Duerstock, Bradley S. and Wachs, Juan P.},
}
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