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Disambiguation of "Turkbey, Evrim"

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A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans.

, , , , and . ABDI@MICCAI, volume 8676 of Lecture Notes in Computer Science, page 103-113. Springer, (2014)

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Evrim Anadol

Basak Evrim Sahin

 

Other publications of authors with the same name

Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94141R. SPIE, (2015)Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging., , , , , , , , , and 9 other author(s). CoRR, (2024)A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling., , , , , and . CoRR, (2015)A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling., , , , , and . IEEE Trans. Image Processing, 26 (1): 386-399 (2017)Rapid artificial intelligence solutions in a pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge., , , , , , , , , and 26 other author(s). Medical Image Anal., (2022)Robust automated lymph node segmentation with random forests., , , , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 90343X. SPIE, (2014)Weakly Supervised One-Stage Vision and Language Disease Detection Using Large Scale Pneumonia and Pneumothorax Studies., , , , , and . MICCAI (4), volume 12264 of Lecture Notes in Computer Science, page 45-55. Springer, (2020)Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation., , , , , , , , , and 7 other author(s). ECCV (21), volume 13681 of Lecture Notes in Computer Science, page 437-455. Springer, (2022)Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 941413. SPIE, (2015)Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan., , , , , , , , , and 10 other author(s). CoRR, (2020)