Nabil Ettehadi - Academia.edu (original) (raw)

Papers by Nabil Ettehadi

Research paper thumbnail of PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer

ArXiv, 2021

Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brai... more Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life. Developmental scientists are particularly interested in the first few years of neurodevelopment. Despite the success of MRI collection and analysis for adults, it is a challenge for researchers to collect high-quality multimodal MRIs from developing infants mainly because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still, and a lack of analysis approaches. These challenges often lead to a significant reduction of usable data. To address this issue, researchers have explored various solutions to replace corrupted scans through synthesizing realistic MRIs. Among them, the convolution neural network (CNN) based generative adversarial network has demonstrated promising results and achieves state-of-the-art performance. However, adversarial training is unstable and may need careful tuning of regulari...

Research paper thumbnail of Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection

ArXiv, 2020

In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) clas... more In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) classification. In the first approach, we introduce a shallow neural network architecture. This model performs well on classification of the most frequent classes while fails at classifying the less frequent ones. In the second approach, we use transfer learning to re-train the last modified layer of a very deep neural network to improve the generalization ability of the model to the less frequent classes. Our results demonstrate superior abilities of transfer learning in DR classification of less frequent classes compared to the shallow neural network.

Research paper thumbnail of Implementation of Feeding Task via Learning from Demonstration

In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-... more In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot's end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

Research paper thumbnail of Near Real-Time Robotic Grasping of Novel Objects in Cluttered Scenes

In this paper, we investigate the problem of grasping novel objects in unstructured environments.... more In this paper, we investigate the problem of grasping novel objects in unstructured environments. Object geometry, reachability, and force closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both MATLAB simulation and experiments using a Kinect One sensor and a Baxter manipulator.

Research paper thumbnail of Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Research paper thumbnail of A learning from demonstration framework for implementation of a feeding task

Encyclopedia with Semantic Computing and Robotic Intelligence

In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-... more In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-assistance agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of the Gaussian mixture model (GMM) are learnt using Gaussian mixture regression (GMR) and expectation maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot’s end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

Research paper thumbnail of PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer

ArXiv, 2021

Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brai... more Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life. Developmental scientists are particularly interested in the first few years of neurodevelopment. Despite the success of MRI collection and analysis for adults, it is a challenge for researchers to collect high-quality multimodal MRIs from developing infants mainly because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still, and a lack of analysis approaches. These challenges often lead to a significant reduction of usable data. To address this issue, researchers have explored various solutions to replace corrupted scans through synthesizing realistic MRIs. Among them, the convolution neural network (CNN) based generative adversarial network has demonstrated promising results and achieves state-of-the-art performance. However, adversarial training is unstable and may need careful tuning of regulari...

Research paper thumbnail of Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection

ArXiv, 2020

In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) clas... more In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) classification. In the first approach, we introduce a shallow neural network architecture. This model performs well on classification of the most frequent classes while fails at classifying the less frequent ones. In the second approach, we use transfer learning to re-train the last modified layer of a very deep neural network to improve the generalization ability of the model to the less frequent classes. Our results demonstrate superior abilities of transfer learning in DR classification of less frequent classes compared to the shallow neural network.

Research paper thumbnail of Implementation of Feeding Task via Learning from Demonstration

In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-... more In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot's end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

Research paper thumbnail of Near Real-Time Robotic Grasping of Novel Objects in Cluttered Scenes

In this paper, we investigate the problem of grasping novel objects in unstructured environments.... more In this paper, we investigate the problem of grasping novel objects in unstructured environments. Object geometry, reachability, and force closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both MATLAB simulation and experiments using a Kinect One sensor and a Baxter manipulator.

Research paper thumbnail of Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Research paper thumbnail of A learning from demonstration framework for implementation of a feeding task

Encyclopedia with Semantic Computing and Robotic Intelligence

In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-... more In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-assistance agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of the Gaussian mixture model (GMM) are learnt using Gaussian mixture regression (GMR) and expectation maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot’s end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.