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Your search keyword '"Najarian, Kayvan"' showing total 101 results
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101 results on '"Najarian, Kayvan"'

1. Automatic Segmentation of Mandibular Ramus and Condyles

Author

Le, Celia, primary, Deleat-Besson, Romain, additional, Prieto, Juan, additional, Brosset, Serge, additional, Dumont, Maxime, additional, Zhang, Winston, additional, Cevidanes, Lucia, additional, Bianchi, Jonas, additional, Ruellas, Antonio, additional, Gomes, Liliane, additional, Gurgel, Marcela, additional, Massaro, Camila, additional, Aliaga-Del Castillo, Aron, additional, Yatabe, Marilia, additional, Benavides, Erika, additional, Soki, Fabiana, additional, Al Turkestani, Najla, additional, Evangelista, Karine, additional, Goncalves, Joao, additional, Valladares-Neto, Jose, additional, Alves Garcia Silva, Maria, additional, Chaves, Cauby, additional, Costa, Fabio, additional, Garib, Daniela, additional, Oh, Heesoo, additional, Gryak, Jonathan, additional, Styner, Martin, additional, Fillion-Robin, Jean-Christophe, additional, Paniagua, Beatriz, additional, Najarian, Kayvan, additional, and Soroushmehr, Reza, additional

Published
2021
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2. Automatic Segmentation of Dental Root Canal and Merging with Crown Shape

Author

Deleat-Besson, Romain, primary, Le, Celia, additional, Al Turkestani, Najla, additional, Zhang, Winston, additional, Dumont, Maxime, additional, Brosset, Serge, additional, Carlos Prieto, Juan, additional, Cevidanes, Lucia, additional, Bianchi, Jonas, additional, Ruellas, Antonio, additional, Gurgel, Marcela, additional, Massaro, Camila, additional, Aliaga-Del Castillo, Aron, additional, Ioshida, Marcos, additional, Yatabe, Marilia, additional, Benavides, Erika, additional, Rios, Hector, additional, Soki, Fabiana, additional, Neiva, Gisele, additional, Fernando Aristizabal, Juan, additional, Rey, Diego, additional, Antonia Alvarez, Maria, additional, Najarian, Kayvan, additional, Gryak, Jonathan, additional, Styner, Martin, additional, Fillion-Robin, Jean-Christophe, additional, Paniagua, Beatriz, additional, and Soroushmehr, Reza, additional

Published
2021
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3. Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

Author

Zhang, Winston, primary, Bianchi, Jonas, additional, Turkestani, Najla Al, additional, Le, Celia, additional, Deleat-Besson, Romain, additional, Ruellas, Antonio, additional, Cevidanes, Lucia, additional, Yatabe, Marilia, additional, Goncalves, Joao, additional, Benavides, Erika, additional, Soki, Fabiana, additional, Prieto, Juan, additional, Paniagua, Beatriz, additional, Najarian, Kayvan, additional, Gryak, Jonathan, additional, and Soroushmehr, Reza, additional

Published
2021
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5. 3D Auto-Segmentation of Mandibular Condyles

Author

Brosset, Serge, primary, Dumont, Maxime, additional, Bianchi, Jonas, additional, Ruellas, Antonio, additional, Cevidanes, Lucia, additional, Yatabe, Marilia, additional, Goncalves, Joao, additional, Benavides, Erika, additional, Soki, Fabiana, additional, Paniagua, Beatriz, additional, Prieto, Juan, additional, Najarian, Kayvan, additional, Gryak, Jonathan, additional, and Soroushmehr, Reza, additional

Published
2020
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7. Detection of Low Cardiac Index Using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks.

Author

Ansari, Sardar, Golbus, Jessica R., Tiba, Mohamad H., Mccracken, Brendan, Wang, Lu, Aaronson, Keith D., Ward, Kevin R., Najarian, Kayvan, and Oldham, Kenn R.

Abstract

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively. [ABSTRACT FROM AUTHOR]

Published
2021
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26. Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome.

Author

Sabeti, Elyas, Drews, Joshua, Reamaroon, Narathip, Warner, Elisa, Sjoding, Michael W., Gryak, Jonathan, and Najarian, Kayvan

Subjects
ADULT respiratory distress syndrome, LABELS, MEDICAL personnel, FAULT diagnosis, UNCERTAINTY, ELECTRONIC health records
Abstract

Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses, affecting 200,000 patients in the United States annually. However, a recent study suggests that most patients with ARDS are diagnosed late or missed completely and fail to receive life-saving treatments. This is primarily due to the dependency of current diagnosis criteria on chest x-ray, which is not necessarily available at the time of diagnosis. In machine learning, such an information is known as Privileged Information - information that is available at training but not at testing. However, in diagnosing ARDS, privileged information (chest x-rays) are sometimes only available for a portion of the training data. To address this issue, the Learning Using Partially Available Privileged Information (LUPAPI) paradigm is proposed. As there are multiple ways to incorporate partially available privileged information, three models built on classical SVM are described. Another complexity of diagnosing ARDS is the uncertainty in clinical interpretation of chest x-rays. To address this, the LUPAPI framework is then extended to incorporate label uncertainty, resulting in a novel and comprehensive machine learning paradigm - Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI). The proposed frameworks use Electronic Health Record (EHR) data as regular information, chest x-rays as partially available privileged information, and clinicians’ confidence levels in ARDS diagnosis as a measure of label uncertainty. Experiments on an ARDS dataset demonstrate that both the LUPAPI and LULUPAPI models outperform SVM, with LULUPAPI performing better than LUPAPI. [ABSTRACT FROM AUTHOR]

Published
2021
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49. Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation.

Author

Mohaghegh, Hoda, Karimi, Nader, Soroushmehr, S. M. Reza, Samavi, Shadrokh, and Najarian, Kayvan

Subjects
GENERALIZATION, COMPUTER vision, ROBOT vision, LEARNING, QUALITATIVE research
Abstract

Estimating scene depth from a single monocular image is a crucial component in computer vision tasks, enabling many further applications such as robot vision, 3-D modeling, and above all, 2-D to 3-D image/video conversion. Since there are an infinite number of possible world scenes, that can produce a unique image, single image depth estimation is a highly challenging task. This paper tackles such an ambiguous problem by using the merits of both global and local information (structures) of a scene. To this end, we formulate single image depth estimation as a regression problem via (on) rich depth related features which describe effective monocular cues. Exploiting the relationship between these image features and depth values is adopted via a learning model which is inspired by modified stacked generalization scheme. The experiments demonstrate competitive results compared with existing data-driven approaches in both quantitative and qualitative analysis with a remarkably simpler approach than previous works. [ABSTRACT FROM AUTHOR]

Published
2019
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50. Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.

Author

Reamaroon, Narathip, Sjoding, Michael W., Lin, Kaiwen, Iwashyna, Theodore J., and Najarian, Kayvan

Subjects
ADULT respiratory distress syndrome, SUPERVISED learning, SUPPORT vector machines, TIME series analysis, ELECTRONIC health records
Abstract

When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient's case or imperfect reliability of the diagnostic criteria. As a result, some cases used in algorithm training may be mislabeled, adversely affecting the algorithm's performance. However, experts may also be able to quantify their diagnostic uncertainty in these cases. We present a robust method implemented with support vector machines (SVM) to account for such clinical diagnostic uncertainty when training an algorithm to detect patients who develop the acute respiratory distress syndrome (ARDS). ARDS is a syndrome of the critically ill that is diagnosed using clinical criteria known to be imperfect. We represent uncertainty in the diagnosis of ARDS as a graded weight of confidence associated with each training label. We also performed a novel time-series sampling method to address the problem of intercorrelation among the longitudinal clinical data from each patient used in model training to limit overfitting. Preliminary results show that we can achieve meaningful improvement in the performance of algorithm to detect patients with ARDS on a hold-out sample, when we compare our method that accounts for the uncertainty of training labels with a conventional SVM algorithm. [ABSTRACT FROM AUTHOR]

Published
2019
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