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Adaptation for Evolving Domains
, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshah, Mahdieh (Supervisor)
Abstract
Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify...
3D Image segmentation with sparse annotation by self-training and internal registration
, Article IEEE Journal of Biomedical and Health Informatics ; 2020 ; Nikdan, M ; Soleymanibaghshah, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...
Incremental evolving domain adaptation
, Article IEEE Transactions on Knowledge and Data Engineering ; Volume 28, Issue 8 , 2016 , Pages 2128-2141 ; 10414347 (ISSN) ; Soleymani Baghshah, M ; Gheisari, M ; Sharif University of Technology
IEEE Computer Society
2016
Abstract
Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space...
3D Image Segmentation with Sparse Annotation by Self-Training and Internal Registration
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 25, Issue 7 , 2021 , Pages 2665-2672 ; 21682194 (ISSN) ; Nikdan, M ; Baghshah, M. S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...
A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks
, Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term...