Search Results - "Farhangi, M. Mehdi"

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans by Farhangi, M. Mehdi, Petrick, Nicholas, Sahiner, Berkman, Frigui, Hichem, Amini, Amir A., Pezeshk, Aria

“…Purpose Multiview two‐dimensional (2D) convolutional neural networks (CNNs) and three‐dimensional (3D) CNNs have been successfully used for analyzing…”
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Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge by Balagurunathan, Yoganand, Beers, Andrew, Mcnitt-Gray, Michael, Hadjiiski, Lubomir, Napel, Sandy, Goldgof, Dmitry, Perez, Gustavo, Arbelaez, Pablo, Mehrtash, Alireza, Kapur, Tina, Yang, Ehwa, Moon, Jung Won, Perez, Gabriel Bernardino, Delgado-Gonzalo, Ricard, Farhangi, M. Mehdi, Amini, Amir A., Ni, Renkun, Feng, Xue, Bagari, Aditya, Vaidhya, Kiran, Veasey, Benjamin, Safta, Wiem, Frigui, Hichem, Enguehard, Joseph, Gholipour, Ali, Castillo, Laura Silvana, Daza, Laura Alexandra, Pinsky, Paul, Kalpathy-Cramer, Jayashree, Farahani, Keyvan

“…Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in…”
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3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS) by Farhangi, M. Mehdi, Frigui, Hichem, Seow, Albert, Amini, Amir A.

“…SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model…”
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Automatic lung nodule detection in thoracic CT scans using dilated slice‐wise convolutions by Farhangi, M. Mehdi, Sahiner, Berkman, Petrick, Nicholas, Pezeshk, Aria

“…Purpose Most state‐of‐the‐art automated medical image analysis methods for volumetric data rely on adaptations of two‐dimensional (2D) and three‐dimensional…”
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Semi‐supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT by Maynord, Michael, Farhangi, M. Mehdi, Fermüller, Cornelia, Aloimonos, Yiannis, Levine, Gary, Petrick, Nicholas, Sahiner, Berkman, Pezeshk, Aria

“…Purpose Machine learning algorithms are best trained with large quantities of accurately annotated samples. While natural scene images can often be labeled…”
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Finding and tracking local communities by approximating derivatives in networks by Rigi, M. Amin, Moser, Irene, Farhangi, M. Mehdi, Lui, Chengfei

“…Since various complex systems are represented by networks, detecting and tracking local communities has become a crucial task nowadays. Local community…”
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Volumetric analysis of respiratory gated whole lung and liver CT data with motion-constrained graph cuts segmentation by Jung Won Cha, Farhangi, M. Mehdi, Dunlap, Neal, Amini, Amir

“…The conventional graph cuts technique has been widely used for image segmentation due to its ability to find the global minimum and its ease of implementation…”
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Deep neural networks-based denoising models for CT imaging and their efficacy by KC, Prabhat, Zeng, Rongping, Farhangi, M. Mehdi, Myers, Kyle J

“…Prabhat KC, Rongping Zeng, M. Mehdi Farhangi, Kyle J. Myers, "Deep neural networks-based denoising models for CT imaging and their efficacy," Proc. SPIE 11595,…”
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DICaugment: A Python Package for 3D Medical Imaging Augmentation by McIntosh, J., Cao, Qian, Sahiner, Berkman, Petrick, Nicholas, Farhangi, M. Mehdi

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Multiple Instance Learning for Malignant vs. Benign Classification of Lung Nodules in Thoracic Screening Ct Data by Safta, Wiem, Farhangi, M. Mehdi, Veasey, Benjamin, Amini, Amir, Frigui, Hichem

“…Multiple Instance Learning (MIL) is proposed for Computer Aided Diagnosis (CADx) without predefined Regions Of Interest (ROIs) from lung cancer screening…”
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Lung Nodule Malignancy Classification Based ON NLSTx Data by Veasey, Benjamin, Farhangi, M. Mehdi, Frigui, Hichem, Broadhead, Justin, Dahle, Michael, Pezeshk, Aria, Seow, Albert, Amini, Amir A.

“…While several datasets containing CT images of lung nodules exist, they do not contain definitive diagnoses and often rely on radiologists' visual assessment…”
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