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您的检索式：作者名="AMIRA ABBES"
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| 1 | Feature Generation and Machine Learning for Robust Multimodal Biometrics 显示文摘 | Bouchaffra Djamel Amira Abbes | 2008 | Pattern Recognition Letters(S0167-8655)2008，41，3： | 1 |
| 2 | FPGA realization of FIR filters by efficient and flexible systolization using distributed arithmetic 显示文摘 | MEHER PRAMOD KUMAR CHANDRASEKARAN SHRUTISAGAR AMIRA ABBES | 2008 | IEEE Transactions on Signal Processing2008，56，71： | 1 |
| 3 | Complete pathologic response after chemoradiotherapy in a patient with rectal squamous cell carcinoma: a case report显示文摘Squamous cell carcinoma(SCC)of the rectum is a rare disease.A 59-year-old man presented with SCC of the middle rectum located 10 cm from the anus.The stage of the tumor was revealed to be T3N+M0.The patient received a combined treatment with cisplatin and fluorouracil in concomitance with external radiation therapy.He then underwent an anterior resection of the rectum.The postoperative histopathological findings classified the tumor as yp T0N0 with cancer-free margins and lymph nodes.Treatment of SCC remains very challenging,and the acquisition of more consistent data is needed. | Zaied Sonia Daldoul Amira Bhiri Hanene Ammar Nouha Khechine Wiem Toumi Omar Abbes Ibtissem Ben Salem Amina Njima Manel Mhabrech Houda | 2017 | Cancer Biology & Medicine2017，14，4： | 0 |
| 4 | Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation显示文摘Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model. | Imene Mecheter Maysam Abbod Habib Zaidi Abbes Amira | 2024 | CAAI Transactions on Intelligence Technology2024，9，1： | 0 |
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