Saeed Masoudnia

Saeed Masoudnia
Research Center for Molecular and Cellular Imaging (RCMCI) · Tehran University of Medical Sciences

PhD of Machine Learning

About

32
Publications
29,140
Reads
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462
Citations
Additional affiliations
February 2012 - June 2013
University of Garmsar
Position
  • Lecturer - Algorithms and Data Structures
January 2012 - January 2014
Young Researchers Club
Position
  • Researcher
September 2010 - December 2010
University of Tehran
Position
  • Teaching Assistantship - Neural Networks course

Publications

Publications (32)
Article
Full-text available
Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, di...
Article
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We ado...
Conference Paper
Full-text available
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in localization information. We excite certain activations in order to help the network learn to better localize (Figure 2). In the later stages of training, we gr...
Article
Full-text available
Purpose: The study aimed to evaluate the postrehabilitation changes in deep gray matter (DGM) nuclei, corticospinal tract (CST), and motor cortex area, involved in motor tasks in patients with ischemic stroke. Methods: Three patients participated in this study, who had experienced an ischemic stroke on the left side of the brain. They underwent...
Article
In recent years, multiple data-driven fiber orientation distribution function (fODF) estimation algorithms and automatic tractography pipelines have been proposed to address the limitations of traditional methods. However, these approaches lack precision and generalizability. To tackle these shortcomings, we introduce CTtrack, a CNN+Transformer-bas...
Conference Paper
Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available re...
Conference Paper
Automatic Brain Tumor Segmentation (BraTS) from MRI plays a key role in diagnosing and treating brain tumors. Although 3D U-Nets achieve state-of-the-art results in BraTS, their clinical use is limited due to requiring high-end GPU with high memory. To address the limitation, we utilize several techniques for customizing a memory-efficient yet accu...
Preprint
Full-text available
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in localization information. We excite certain activations in order to help the network learn to better localize. In the later stages of training, we gradually red...
Preprint
Full-text available
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt o...
Preprint
Full-text available
Offline Signature Verification (OSV) remains a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during the training. This challenge is aggravated when there are small labeled training data available but with large intra-personal variations. In this study, we address this issue by employing...
Preprint
Full-text available
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning a...
Poster
Full-text available
Most Brain-inspired Visual Object Recognition Models(BVORMs) do not consider local and global reciprocal connections in the visual pathway. We addressed this weakness and implemented an attention modulation mechanism based on the feedback connections in BVORMs, where feature-selectivity is shaped and modulated by categorization of objects based on...
Article
Full-text available
Both theoretical and experimental studies have shown that combining accurate neural networks (NNs) in the ensemble with negative error correlation greatly improves their generalization abilities. Negative correlation learning (NCL) and mixture of experts (ME), two popular combining methods, each employ different special error functions for the simu...
Article
Full-text available
Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively co...
Article
Full-text available
A modified version of Boosted Mixture of Experts BME for low-resolution face recognition is presented in this paper. Most of the methods developed for low-resolution face recognition focused on improving the resolution of face images and/or special feature extraction methods that can deal effectively with low-resolution problem. However, we focused...
Conference Paper
Full-text available
In this paper, we present a modified feature extraction and an improved combining classifiers method to analyse, model and classify plants growth process. Plants growth is a significant issue in different aspects in biology. Arabidopsis Thaliana is a plant that is very much interesting, because its genetic structure has some similarities with that...
Article
Full-text available
This paper presents a new method to automate the process of epileptic seizure detection in electroencephalogram (EEG) signals using wavelet transform and an improved version of negative correlation learning (NCL) algorithm. An improved version of NCL is proposed by incorporating the capability of gating network, as a dynamic combining part of the m...
Article
Full-text available
A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time ser...
Article
Full-text available
Negative Correlation Learning (NCL) is a popular combining method that employs special error function for the simultaneous training of base neural network (NN) experts. In this article, we propose an improved version of NCL method in which the capability of gating network, as the combining part of Mixture of Experts method, is used to combine the b...
Article
Full-text available
This paper investigates the effect of diversity caused by Negative Correlation Learning (NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates and Averaging, as two non-trainable combining methods and Stacked Generalization as a trainable combiner are investigated in our exp...
Conference Paper
Full-text available
In this paper a holistic method and a local method based on decision template ensemble are investigated. In addition by combining both methods, a new hybrid method for boosting the performance of the system is proposed and evaluated with respect to robustness against small sample size problem in face recognition. Inadequate and substantial variatio...
Conference Paper
Full-text available
Mixture of Experts (ME) is a modular neural network architecture for supervised learning. In this paper, we propose an evidence-based ME to deal with the classification problem. In the basic form of ME the problem space is automatically divided into several subspaces for the experts and the outputs of experts are combined by a gating network. Satis...
Article
Full-text available
UTUtd-3D 2006 is based on experience of UTUtd-3D 2005 [1]. Last year the team worked on high level skills especially on position-ing, ball handling, decision making[2]. As the server status for the 2006 competition is cleared recently, this paper mostly describes changes since 2005 in the architecture of the UTUtd-3d team and the planned changes fo...
Article
Full-text available
UTUtd-3D 2006 is based on experience of UTUtd-3D 2005 (1). Last year the team worked on high level skills especially on position- ing, ball handling, decision making(2). As the server status for the 2006 competition is cleared recently, this paper mostly describes changes since 2005 in the architecture of the UTUtd-3d team and the planned changes f...

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Projects

Projects (2)
Project
In this project, we aim to tackle the challenges of Offline Signature Verification by Convolutional Network through different learning paradigms, e.g. Active learning, Transfer learning and Ensemble learning.
Project
"It doesn’t matter if deep learning mimics the brain or Watson is cognitive. It matters if they work", Derrick Harris