Saba Heidari Gheshlaghi | AmirKabir University Of Technology (original) (raw)

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Papers by Saba Heidari Gheshlaghi

Research paper thumbnail of Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Research paper thumbnail of Age encoded adversarial learning for pediatric CT segmentation

Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring... more Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring, pre-operative planning, radiation therapy planning and CT dose estimation. However, data limitation remains one of the main challenges in the medical image segmentation domain. This challenge is particularly huge in pediatric medical imaging due to the patients' heightened sensitivity to radiation. To address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7. CFG-SegNet achieves an average segmentation accuracy of 0.681 DSC (Dice Similarit...

Research paper thumbnail of Multiple Sclerosis Diagnosis with Fuzzy C-Means

Computer Science & Information Technology, Jan 27, 2018

Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis... more Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

International Journal of Artificial Intelligence & Applications

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical in... more Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection

arXiv: Image and Video Processing, 2019

 Abstract—Automated and accurate classification of Magnetic Resonance Images (MRI) comes into ac... more  Abstract—Automated and accurate classification of Magnetic Resonance Images (MRI) comes into account in medical analysis significantly, interpretation and improving the efficiency of healthcare in MS patients. Current study proposes a novel automatic classification system to distinguish Multiple Sclerosis’s patients from healthy subjects by using their MR brain images. Herein, Support Vector Machine (SVM) as an effective classifier with Polynomial kernels is applied to have better performance in distinguishing specified decision classes. Furthermore, some methods come into account to have appropriate results such as discrete wavelet decomposition (DWT) which extracts local information from analyzing MR images, the superpixel segmentation to have automatic image partitioning which processed through principal components analyses method to deal with data dimensionality problem. The proposed method is tested with 10-fold cross validations method to check the final accuracy. Experiment...

Research paper thumbnail of OCT Image Segmentation Using Neural Architecture Search and SRGAN

2020 25th International Conference on Pattern Recognition (ICPR), 2021

Medical image segmentation is a critical field in the domain of computer vision and with the grow... more Medical image segmentation is a critical field in the domain of computer vision and with the growing acclaim of deep learning based models, research in this field is constantly expanding. Optical coherence tomography (OCT) is a non-invasive method that scans the human's retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution gene...

Research paper thumbnail of Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

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

Research paper thumbnail of Efficient Oct Image Segmentation Using Neural Architecture Search

2020 IEEE International Conference on Image Processing (ICIP), 2020

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Opt... more In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and preprocessed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method is applied to make the search strategy practical for the task in hand. We empirically evaluated our method on our in-house OCT dataset. The experimental results demonstrate that the self-adapting NAS-Unet architecture substantially outperformed the competitive human-designed architecture by achieving 95.4% in mean Intersection over Union metric and 78.7% in Dice similarity coefficient.

Research paper thumbnail of SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANS

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical in... more Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANS

Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis... more Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Research paper thumbnail of Age encoded adversarial learning for pediatric CT segmentation

Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring... more Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring, pre-operative planning, radiation therapy planning and CT dose estimation. However, data limitation remains one of the main challenges in the medical image segmentation domain. This challenge is particularly huge in pediatric medical imaging due to the patients' heightened sensitivity to radiation. To address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7. CFG-SegNet achieves an average segmentation accuracy of 0.681 DSC (Dice Similarit...

Research paper thumbnail of Multiple Sclerosis Diagnosis with Fuzzy C-Means

Computer Science & Information Technology, Jan 27, 2018

Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis... more Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

International Journal of Artificial Intelligence & Applications

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical in... more Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection

arXiv: Image and Video Processing, 2019

 Abstract—Automated and accurate classification of Magnetic Resonance Images (MRI) comes into ac... more  Abstract—Automated and accurate classification of Magnetic Resonance Images (MRI) comes into account in medical analysis significantly, interpretation and improving the efficiency of healthcare in MS patients. Current study proposes a novel automatic classification system to distinguish Multiple Sclerosis’s patients from healthy subjects by using their MR brain images. Herein, Support Vector Machine (SVM) as an effective classifier with Polynomial kernels is applied to have better performance in distinguishing specified decision classes. Furthermore, some methods come into account to have appropriate results such as discrete wavelet decomposition (DWT) which extracts local information from analyzing MR images, the superpixel segmentation to have automatic image partitioning which processed through principal components analyses method to deal with data dimensionality problem. The proposed method is tested with 10-fold cross validations method to check the final accuracy. Experiment...

Research paper thumbnail of OCT Image Segmentation Using Neural Architecture Search and SRGAN

2020 25th International Conference on Pattern Recognition (ICPR), 2021

Medical image segmentation is a critical field in the domain of computer vision and with the grow... more Medical image segmentation is a critical field in the domain of computer vision and with the growing acclaim of deep learning based models, research in this field is constantly expanding. Optical coherence tomography (OCT) is a non-invasive method that scans the human's retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution gene...

Research paper thumbnail of Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

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

Research paper thumbnail of Efficient Oct Image Segmentation Using Neural Architecture Search

2020 IEEE International Conference on Image Processing (ICIP), 2020

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Opt... more In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and preprocessed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method is applied to make the search strategy practical for the task in hand. We empirically evaluated our method on our in-house OCT dataset. The experimental results demonstrate that the self-adapting NAS-Unet architecture substantially outperformed the competitive human-designed architecture by achieving 95.4% in mean Intersection over Union metric and 78.7% in Dice similarity coefficient.

Research paper thumbnail of SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANS

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical in... more Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

Research paper thumbnail of MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANS

Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis... more Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.