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(1 - 7 of 7)
LIGHTLY SUPERVISED MACHINE LEARNING FOR CLASSIFYING ONLINE SOCIAL DATA
Title
LIGHTLY SUPERVISED MACHINE LEARNING FOR CLASSIFYING ONLINE SOCIAL DATA
Creator
Mohammady Ardehaly, Ehsan
Date
2017, 2017-05
Description
Classifying latent attributes of social media users has many applications in public health, politics, and marketing. For example, web-based...
Show moreClassifying latent attributes of social media users has many applications in public health, politics, and marketing. For example, web-based studies of public health require monthly estimates of the health status and demographics of users based on their public communications. Most existing approaches are based on supervised learning. Supervised learning requires human annotated labeled data, which can be expensive and many attributes such as health are hard to annotate at the user level. In this thesis, we investigate classification algorithms that use population statistical constraints such as demographics, names, polls, and social network followers to predict individual user attributes. For example, the racial makeup of counties is a source of light supervision came from the U.S. Census to train classification models. These statistics are usually easy to obtain, and a large amount of unlabeled data from social media sites (e.g. Twitter) are available. Learning from Label Proportions (LLP) is a lightly supervised approach when the training data is multiple sets of unlabeled samples and only label distributions of them are known. Because social media users are not a representative sample of the population and constraints are too noisy, using existing LLP models (e.g. linear models, label regularization) is insufficient. We develop several new LLP algorithms to extend LLP to deal with this bias, including bag selection and robust classification models. Also, we propose a scalable model to infer political sentiment from the high temporal big data, and estimate the daily conditional probability of different attributes as a supplement method to polls, for social scientists. Because, constraints are not often available in some domains (e.g. blogs), we propose a self-training algorithm to gradually adapt a classifier trained on social media to a different but similar field. We also extend our framework to deep learning and provide empirical results for demographic classification using the user profile image. Finally, when both textual and profile image are available for a user, we provide a co-training algorithm to iteratively improve both image and text classifications accuracy, and apply an ensemble method to achieve the highest precision.
Ph.D. in Computer Science, May 2017
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LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Title
LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator
Song, Chao
Date
2019
Description
Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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DEEP LEARNING IMAGE-DENOISING FOR IMPROVING DIAGNOSTIC ACCURACY IN CARDIAC SPECT
Title
DEEP LEARNING IMAGE-DENOISING FOR IMPROVING DIAGNOSTIC ACCURACY IN CARDIAC SPECT
Creator
Liu, Junchi
Date
2022
Description
Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized...
Show moreMyocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized for diagnosis of coronary artery diseases (CAD) in nuclear medicine. Because of the concern of potential radiation risks, the imaging dose administered to patients is limited in SPECT-MPI. Due to the low count statistics in acquired data, SPECT images can suffer from high levels of noise. In this study, we investigate the potential benefit of applying deep learning (DL) techniques for denoising in SPECT-MPI studies. Owing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in full-dose studies. Afterwards, we investigate the benefit of applying N2N DL on reduced-dose studies to improve the detection accuracy of perfusion defects. To address the great variability in noise level among different subjects, we propose a scheme to account for the inter-subject variabilities in training a DL denoising network to improve its generalizability. In addition, we propose a dose-blind training approach for denoising at multiple reduced-dose levels. Moreover, we investigate several training schemes to address the issue that defect and non-defect image regions are highly unbalanced in a data set, where the overwhelming majority by non-defect regions tends to have a more pronounced contribution to the conventional loss function. We investigate whether these training schemes can effectively improve preservation of perfusion defects and yield better defect detection accuracy. In the experiments we demonstrated the proposed approaches with a set of 895 clinical acquisitions. The results show promising performance in denoising and improving the detectability of perfusion-defects with the proposed approaches.
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DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Title
DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Creator
Yuan, Yu
Date
2022
Description
For decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data...
Show moreFor decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data analysis and pattern recognition applications where analytical methods are ineffective. In recent years, Deep Learning (DL) has been proven to be an effective AI technique that can outperform other methods in applications such as computer vision, natural language processing, autonomous driving. Realizing the potential of deep learning techniques, researchers have also started to apply deep learning on other industrial applications. Today, deep learning based models are used to innovate and accelerate automation, guidance, and decision making in various industries including automotive industry, pharmaceutical industry, finance, agriculture and more. In this research, several important industrial applications (on Biomedicine and Non-Destructive Testing) utilizing deep learning algorithms will be introduced and analyzed. The first biopharmaceutical application focuses on developing a deep learning based model to automate the visual inspection process in Median Tissue Culture Infectious Dose(TCID50). TCID50 is one of the most popular methods for viral quantification. An important step of TCID50 is to visually inspect the sample and decide if it exhibits cytopathic effect(CPE) or not. Two novel models have been developed to detect CPE in microscopic images of cell culture in 96 well-plates. The first model consists of a convolutional neural network (CNN) and support vector machine(SVM). The second model is a fully convolutional network (FCN) followed by morphological post-processing steps. The models are tested on 4 cell lines and achieve very high accuracy. Another biopharmaceutical application developed for cellular microscopic images is the clonal selection. Clonal selection is one of the mandatory steps in cell line development process. It focuses on verifying the clonality of the cell culture. The researchers used to visually inspect the microscopic images to verify the clonality. In this work, a novel deep learning based model and a workflow is developed to accelerate the process. This algorithm consists of multiple steps, including image analysis after incubation to detect the cell colonies, and verify its clonality in day0 image. The results and common mis-classification cases are shown in this thesis. Image analysis method is not the only technology that has been advancing for cellular image analysis in biopharmaceutical industry. A new class of instruments are currently used in biopharmaceutical industry which enable more opportunities for image analysis. To make the most of these new instruments, a convolutional neural network based architecture is used to perform accurate cell counting and cell morphology based segmentation. This analysis can provide more insight of the cells at very early stage in characterization process of cell line development. The architecture and the testing results are presented in this work. The proposed algorithm has achieved very high accuracy on both applications, and the cell morphology based segmentation enables a brand new feature for scientists to predict the potential productivity of the cells. Next part of this dissertation is focused on hardware implementation of Ultrasonic Non-Destructive Testing (NDT) methods based on deep learning, which can be highly useful in flaw detection and classification applications. With the help of a smart and mobile Non-Destructive Testing device, engineers can accurately detect and locate the flaws inside the materials without reliance on high performance computation resources. The first NDT application presents a hardware implementation of a deep learning algorithm on Field-programmable gate array(FPGA) for Ultrasound flaw detection. The Ultrasound flaw detection algorithm consists of a wavelet transform followed by a LeNet inspired convolutional neural network called Ultra-LeNet. This work is focused on implementing the computationally difficult part of this algorithm: Ultra-LeNet, so that it can be used in the field where high performance computation resources (e.g., AWS) are not accessible. The implementation uses resource partitioning to design two dedicated pipelined accelerators for convolutional layers and fully connected layers respectively. Both accelerators utilize loop unrolling, loop pipelining and batch processing techniques to maximize the throughput. The comparison to other work has shown that the implementation has achieved higher hardware utilization efficiency. The second NDT application is also focused on implementing a deep learning based algorithm for Ultrasound flaw detection on a FPGA. Instead of implementing the Ultra-LeNet, the deep learning model used in this application is Meta-learning based Siamese Network, which is capable for multi-class classification and it can also classify a new class even if it does not appear in the training dataset with the help of automated learning features. The hardware implementation is significantly different than the previous algorithm. In order to improve the inference operation efficiency, the model is compressed with both pruning and quantization, and the FPGA implementation is specifically designed to accelerate the compressed CNN with high efficiency. The CNN model compression method and hardware design are novel methods introduced in this work. Comparison against other compressed CNN accelerators is also presented.
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Exploiting contextual information for deep learning based object detection
Title
Exploiting contextual information for deep learning based object detection
Creator
Zhang, Chen
Date
2020
Description
Object detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great...
Show moreObject detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great progress made in recent years, object detection is still a challenging task. One of the keys to improving the performance of object detection is to utilize the contextual information from the image itself or from a video sequence. Contextual information is defined as the interrelated condition in which something exists or occurs. In object detection, such interrelated condition can be related background/surroundings, support from image segmentation task, and the existence of the object in the temporal domain for video-based object detection. In this thesis, we propose multiple methods to exploit contextual information to improve the performance of object detection from images and videos.First, we focus on exploiting spatial contextual information in still-image based object detection, where each image is treated independently. Our research focuses on extracting contextual information using different approaches, which includes recurrent convolutional layer with feature concatenation (RCL-FC), 3-D recurrent neural networks (3-D RNN), and location-aware deformable convolution. Second, we focus on exploiting pixel-level contextual information from a related computer vision task, namely image segmentation. Our research focuses on applying a weakly-supervised auxiliary multi-label segmentation network to improve the performance of object detection without increasing the inference time. Finally, we focus on video object detection, where the temporal contextual information between video frames are exploited. Our first research involves modeling short-term temporal contextual information using optical flow and modeling long-term temporal contextual information using convLSTM. Another research focuses on creating a two-path convLSTM pyramid to handle multi-scale temporal contextual information for dealing with the change in object's scale. Our last work is the event-aware convLSTM that forces convLSTM to learn about the event that causes the performance to drop in a video sequence.
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LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Title
LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator
Song, Chao
Date
2019
Description
Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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Voxel Transformer with Density-Aware Deformable Attention for 3D Object Detection
Title
Voxel Transformer with Density-Aware Deformable Attention for 3D Object Detection
Creator
Kim, Taeho
Date
2023
Description
The Voxel Transformer (VoTr) is a prominent model in the field of 3D object detection, employing a transformer-based architecture to...
Show moreThe Voxel Transformer (VoTr) is a prominent model in the field of 3D object detection, employing a transformer-based architecture to comprehend long-range voxel relationships through self-attention. However, despite its expanded receptive field, VoTr’s flexibility is constrained by its predefined receptive field. In this paper, we present a Voxel Transformer with Density-Aware Deformable Attention (VoTr-DADA), a novel approach to 3D object detection. VoTr-DADA leverages density-guided deformable attention for a more adaptable receptive field. It efficiently identifies key areas in the input using density features, combining the strengths of both VoTr and Deformable Attention. We introduce the Density-Aware Deformable Attention (DADA) module, which is specifically designed to focus on these crucial areas while adaptively extracting more informative features. Experimental results on the KITTI dataset and the Waymo Open dataset show that our proposed method outperforms the baseline VoTr model in 3D object detection while maintaining a fast inference speed.
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