Publications

@InProceedings{10.1007/978-3-031-73376-5_12, author="Amer Alyaa, Hussein Alaa, Ahmadvand Noushin, Magdy Sahar, Abdi Abas,Serej Nasim Dadashi, Ghatwary Noha, Azarmehr Neda", editor="Ali Sharib, van der Sommen, Fons and Papie{\.{z}}, Bart{\l}omiej W{\l}adys{\l}aw, and Ghatwary Noha and Jin, Yueming and Kolenbrander, Iris", title="Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 Network", booktitle="Cancer Prevention, Detection, and Intervention", year="2025", publisher="Springer Nature Switzerland", address="Cham", pages="124--132", abstract="Colon cancer is among the leading causes of cancer-related death worldwide for both men and women, with colorectal polyps serving as a significant predisposing factor. Early polyp identification and removal-the precursors to colorectal cancer-is essential to its prevention. Colonoscopy is considered the gold standard for colorectal cancer screening because it allows for the immediate removal of polyps, preventing them from developing into cancer. Despite its effectiveness, conventional colonoscopy is time-consuming, highly labor-intensive, and prone to human mistakes. Therefore, we modified the efficient object detection model, YOLO-V8, to develop our novel approach, EDF-YOLO8, for automating polyp identification. Our model employs deformable convolution in the bottleneck as a robust solution for effectively detecting polyps of various sizes. We enhance the effectiveness of our model by incorporating the Exponential Linear Unit (ELU), which further increases the detection accuracy and tends to accelerate the model learning process. We trained and tested the suggested model on two distinct datasets from publicly accessible sources and conducted thorough assessments to ensure its robustness and generalizability. The proposed model achieved an outstanding performance, attaining a mAP50 score of 0.931 and 0.894 for the Kvasir and Polypgen datasets, respectively. Performance analysis demonstrates the efficiency and robustness of our model in accurately detecting polyps from colonoscopic frames from different datasets.", isbn="978-3-031-73376-5" }

@inproceedings{Kyi2024mitotic, title={Enhancing Mitotic Figure Detection Using Attention Modules in Digital Pathology}, author={Kyi, Hlaing Kyi and Zolgharni, Massoud and Khurram, Syed Ali and Azarmehr, Neda}, booktitle={Medical Image Understanding and Analysis (MIUA)}, year={2024}, month={October}, address={Manchester, UK}, publisher={Frontiers Media}, pages={130--135}, doi={10.3389/978-2-8325-1244-9} }

@article{Alajrami2024active, title={Active Learning for Left Ventricle Segmentation in Echocardiography}, author={Alajrami, Eman and Ng, Tiffany and Jevsikov, Jevgeni and Naidoo, Preshen and Fernandes, Patricia and Azarmehr, Neda and Dinmohammadi, Fateme and Shun-Shin, Matthew J and Dadashi Serej, Nasim and Francis, Darrel P and Zolgharni, Massoud}, journal={Computer Methods and Programs in Biomedicine}, volume={248}, pages={108111}, year={2024}, publisher={Elsevier}, doi={10.1016/j.cmpb.2024.108111} }

@article{Jevsikov2024mitral, title={Automated Mitral Inflow Doppler Peak Velocity Measurement Using Deep Learning}, author={Jevsikov, Jevgeni and Ng, Tiffany and Lane, Elisabeth S and Alajrami, Eman and Naidoo, Preshen and Fernandes, Patricia and Sehmi, Joban S and Alzetani, Maysaa and Demetrescu, Camelia D and Azarmehr, Neda and Dadashi Serej, Nasim and Stowell, Catherine C and Shun-Shin, Matthew J and Francis, Darrel P and Zolgharni, Massoud}, journal={Computers in Biology and Medicine}, volume={171}, pages={108192}, year={2024}, publisher={Pergamon}, doi={10.1016/j.compbiomed.2024.108192} }

@article{Bashir2023lymphocytic, title={A Digital Score of Peri‐Epithelial Lymphocytic Activity Predicts Malignant Transformation in Oral Epithelial Dysplasia}, author={Bashir, Raja Muhammad Saad and Shephard, Adam J and Mahmood, Hanya and Azarmehr, Neda and Raza, Shan E Ahmed and Khurram, Syed Ali and Rajpoot, Nasir M}, journal={The Journal of Pathology}, volume={260}, number={4}, pages={431--442}, year={2023}, month={August}, publisher={John Wiley \& Sons, Ltd}, doi={10.1002/path.6030} }

@inproceedings{Azarmehr2019, title={Automated Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning}, author={Azarmehr, Neda and Ye, Xujiong and Janan, Faraz and Howard, James P. and Francis, Darrel P. and Zolgharni, Massoud}, booktitle={Medical Image Understanding and Analysis (MIUA)}, year={2019} }