ダウドビレル

ダウドビレル | Daoud Bilel

所属機関・部署：
職名：特任助教

研究分野 (3件)：医用システム , 生命、健康、医療情報学 , 知覚情報処理

研究キーワード (4件)：医用画像処理 , 放射線療法の計画と治療 , コンピュータ支援診断・手術 , 計算解剖学

論文 (7件)：

Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud. A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). 2021. 3256-3263
B. Daoud, K. Morooka, R. Kurazume, N. Fourati, W. Mnejja, L. Farhat, J. Daoud. PO-1668: Can we use cascade deep learning for GTV delineation in adaptive radiotherapy for NPC?. Radiotherapy and Oncology. 2020. 152. S916-S916
Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud. A Method for Predicting Dose Distribution of Nasopharyngeal Carcinoma Cases by Multiple Deep Neural Networks. 2020 JOINT 9TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2020 4TH INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR). 2020
Daoud, Bilel, Khalfallah, Ali, Farhat, Leila, Mnejja, Wafa, Morooka, Ken'ichi, Bouhlel, Med Salim, Daoud, Jamel. Automatic segmentation of Nasopharyngeal carcinoma from CT images. International Journal of Biomedical Engineering and Technology. 2020. 33. 3. 240-257
Daoud, Bilel, Morooka, Ken’ichi, Kurazume, Ryo, Leila, Farhat, Mnejja, Wafa, Daoud, Jamel. 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. Computerized Medical Imaging and Graphics. 2019. 77. 101644-101644

学歴 (1件)：

経歴 (2件)：

所属学会 (1件)：

IEEE

※　J-GLOBALの研究者情報は、researchmapの登録情報に基づき表示しています。登録・更新については、こちらをご覧ください。

ダウド ビレル