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Disambiguation of "Ahmadi, Seyed-Ahmad"

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Robust Segmentation of Various Anatomies in 3D Ultrasound Using Hough Forests and Learned Data Representations.

, , , , , , , , and . MICCAI (2), volume 9350 of Lecture Notes in Computer Science, page 111-118. Springer, (2015)

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Seyed-Ahmad Ahmadi

Ahmad Ahmadi

Ahmad Seyed-Mozaffari

Seyed Ahmad Hosseinizadeh

Seyed Ahmad Ali Golriz

 

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A diffusion-based connectivity map of the GPi for optimised stereotactic targeting in DBS., , , , , , and . NeuroImage, (2017)3D transcranial ultrasound as a novel intra-operative imaging technique for DBS surgery: a feasibility study., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 10 (6): 891-900 (2015)Domain-Specific Loss Design for Unsupervised Physical Training: A New Approach to Modeling Medical ML Solutions., , , , , , , , and . MICCAI (2), volume 12262 of Lecture Notes in Computer Science, page 540-550. Springer, (2020)Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data., , , , , , , , and . NeuroImage, (2022)Robust Segmentation of Various Anatomies in 3D Ultrasound Using Hough Forests and Learned Data Representations., , , , , , , , and . MICCAI (2), volume 9350 of Lecture Notes in Computer Science, page 111-118. Springer, (2015)Diabetes60 - Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks., , , , , , , , and . ICCV Workshops, page 1526-1535. IEEE Computer Society, (2017)Rigid US-MRI Registration Through Segmentation of Equivalent Anatomic Structures - A Feasibility Study using 3D Transcranial Ultrasound of the Midbrain., , , , and . Bildverarbeitung für die Medizin, page 410-415. Springer, (2012)Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks., , , , , , , , , and 10 other author(s). CoRR, (2017)InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction., , , , , , , , and . IPMI, volume 11492 of Lecture Notes in Computer Science, page 73-85. Springer, (2019)SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks., , , , , , , , , and . ISBI, page 839-843. IEEE, (2017)