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    No-Reference image quality assessment using transfer learning

    , Article 9th International Symposium on Telecommunication, IST 2018, 17 December 2018 through 19 December 2018 ; 2019 , Pages 637-640 ; 9781538682746 (ISBN) Otroshi Shahreza, H ; Amini, A ; Behroozi, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    With the recent advancements in deep learning, high performance neural networks have been introduced. These neural networks also can be used to solve similar problems in a transfer learning approach. Recently, several state-of-The-Art Convolutional Neural Networks (CNNs) are proposed for computer vision tasks. On the other hand, in-The-wild No-Reference (Blind) Image Quality Assessment (NR-IQA) problem is known as a challenging human perceptual problem. In this paper, a transfer learning approach is used to solve the problem of in-The-wild NR-IQA. With a few training times, the proposed neural network exceeds all the previous methods which are not using deep neural networks. Further, the... 

    Esophageal gross tumor volume segmentation using a 3D convolutional neural network

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16 September 2018 through 20 September 2018 ; Volume 11073 LNCS , 2018 , Pages 343-351 ; 03029743 (ISSN); 9783030009366 (ISBN) Yousefi, S ; Sokooti, H ; Elmahdy, M. S ; Peters, F. P ; Manzuri Shalmani, M. T ; Zinkstok, R. T ; Staring, M ; Sharif University of Technology
    Springer Verlag  2018
    Abstract
    Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighboring tissues in CT scans, this problem has been addressed weakly. In this paper, we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverage design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to... 

    CNNdroid: GPU-accelerated execution of trained deep convolutional neural networks on android

    , Article 24th ACM Multimedia Conference, MM 2016, 15 October 2016 through 19 October 2016 ; 2016 , Pages 1201-1205 ; 9781450336031 (ISBN) Latifi Oskouei, S. S ; Golestani, H ; Hashemi, M ; Ghiasi, S ; Sharif University of Technology
    Association for Computing Machinery, Inc  2016
    Abstract
    Many mobile applications running on smartphones and wear- able devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU- accelerated library, dubbed CNNdroid [1], for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https://github.com/ENCP/CNNdroid  

    Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (AAA) using convolutional neural networks and hough circles algorithm

    , Article Cardiovascular Engineering and Technology ; Volume 10, Issue 3 , 2019 , Pages 490-499 ; 1869408X (ISSN) Mohammadi, S ; Mohammadi, M ; Dehlaghi, V ; Ahmadi, A ; Sharif University of Technology
    Springer New York LLC  2019
    Abstract
    Purpose: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients’ lives due to its rupturing risk, so prompt recognition and diagnosis of this disorder is vital. Although computed tomography angiography (CTA) is the preferred imaging modality used by radiologist for diagnosing AAA, computed tomography (CT) images can be used too. In the recent decade, there has been several methods suggested by experts in order to find a precise automated way to diagnose AAA without human intervention base on CT and CTA images. Despite great...