Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Raza, Shan E. Ahmed"'
Early detection of cancer can help improve patient prognosis by early intervention. Head and neck cancer is diagnosed in specialist centres after a surgical biopsy, however, there is a potential for these to be missed leading to delayed diagnosis. To
Externí odkaz:
http://arxiv.org/abs/2405.01937
Whole Slide Images (WSIs) provide exceptional detail for studying tissue architecture at the cell level. To study tumour microenvironment (TME) with the context of various protein biomarkers and cell sub-types, analysis and registration of features u
Externí odkaz:
http://arxiv.org/abs/2404.17041
Autor:
Vu, Quoc Dang, Fong, Caroline, Gordon, Anderley, Lund, Tom, Silveira, Tatiany L, Rodrigues, Daniel, von Loga, Katharina, Raza, Shan E Ahmed, Cunningham, David, Rajpoot, Nasir
Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide. In OG cancers, recent studies have showed that PDL1 immune checkpoint inhibitors (ICI) in combination with chemotherapy improves patient survival. However, our
Externí odkaz:
http://arxiv.org/abs/2402.19296
Autor:
Al-Rubaian, Arwa, Gunesli, Gozde N., Althakfi, Wajd A., Azam, Ayesha, Rajpoot, Nasir, Raza, Shan E Ahmed
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor behavior and has a significant impact on patient prognosis. In this work,
Externí odkaz:
http://arxiv.org/abs/2311.15847
Autor:
Shephard, Adam J, Jahanifar, Mostafa, Wang, Ruoyu, Dawood, Muhammad, Graham, Simon, Sidlauskas, Kastytis, Khurram, Syed Ali, Rajpoot, Nasir M, Raza, Shan E Ahmed
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. In this study, we introduce an innovative deep learning pipelin
Externí odkaz:
http://arxiv.org/abs/2311.06185
Autor:
Shephard, Adam J, Mahmood, Hanya, Raza, Shan E Ahmed, Araujo, Anna Luiza Damaceno, Santos-Silva, Alan Roger, Lopes, Marcio Ajudarte, Vargas, Pablo Agustin, McCombe, Kris, Craig, Stephanie, James, Jacqueline, Brooks, Jill, Nankivell, Paul, Mehanna, Hisham, Khurram, Syed Ali, Rajpoot, Nasir M
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, resulting in the under/over-treatment of patients. We developed a new Tr
Externí odkaz:
http://arxiv.org/abs/2311.05452
Autor:
Jahanifar, Mostafa, Raza, Manahil, Xu, Kesi, Vuong, Trinh, Jewsbury, Rob, Shephard, Adam, Zamanitajeddin, Neda, Kwak, Jin Tae, Raza, Shan E Ahmed, Minhas, Fayyaz, Rajpoot, Nasir
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming fr
Externí odkaz:
http://arxiv.org/abs/2310.19656
Autor:
Shephard, Adam J, Bashir, Raja Muhammad Saad, Mahmood, Hanya, Jahanifar, Mostafa, Minhas, Fayyaz, Raza, Shan E Ahmed, McCombe, Kris D, Craig, Stephanie G, James, Jacqueline, Brooks, Jill, Nankivell, Paul, Mehanna, Hisham, Khurram, Syed Ali, Rajpoot, Nasir M
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer variability, and does not reliably predict malignancy progression, potentia
Externí odkaz:
http://arxiv.org/abs/2307.03757
Autor:
Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Weigert, Martin, Schmidt, Uwe, Zhang, Wenhua, Zhang, Jun, Yang, Sen, Xiang, Jinxi, Wang, Xiyue, Rumberger, Josef Lorenz, Baumann, Elias, Hirsch, Peter, Liu, Lihao, Hong, Chenyang, Aviles-Rivero, Angelica I., Jain, Ayushi, Ahn, Heeyoung, Hong, Yiyu, Azzuni, Hussam, Xu, Min, Yaqub, Mohammad, Blache, Marie-Claire, Piégu, Benoît, Vernay, Bertrand, Scherr, Tim, Böhland, Moritz, Löffler, Katharina, Li, Jiachen, Ying, Weiqin, Wang, Chixin, Kainmueller, Dagmar, Schönlieb, Carola-Bibiane, Liu, Shuolin, Talsania, Dhairya, Meda, Yughender, Mishra, Prakash, Ridzuan, Muhammad, Neumann, Oliver, Schilling, Marcel P., Reischl, Markus, Mikut, Ralf, Huang, Banban, Chien, Hsiang-Chin, Wang, Ching-Ping, Lee, Chia-Yen, Lin, Hong-Kun, Liu, Zaiyi, Pan, Xipeng, Han, Chu, Cheng, Jijun, Dawood, Muhammad, Deshpande, Srijay, Bashir, Raja Muhammad Saad, Shephard, Adam, Costa, Pedro, Nunes, João D., Campilho, Aurélio, Cardoso, Jaime S., S, Hrishikesh P, Puthussery, Densen, G, Devika R, C V, Jiji, Zhang, Ye, Fang, Zijie, Lin, Zhifan, Zhang, Yongbing, Lin, Chunhui, Zhang, Liukun, Mao, Lijian, Wu, Min, Vo, Vi Thi-Tuong, Kim, Soo-Hyung, Lee, Taebum, Kondo, Satoshi, Kasai, Satoshi, Dumbhare, Pranay, Phuse, Vedant, Dubey, Yash, Jamthikar, Ankush, Vuong, Trinh Thi Le, Kwak, Jin Tae, Ziaei, Dorsa, Jung, Hyun, Miao, Tianyi, Snead, David, Raza, Shan E Ahmed, Minhas, Fayyaz, Rajpoot, Nasir M.
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest
Externí odkaz:
http://arxiv.org/abs/2303.06274
Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmen
Externí odkaz:
http://arxiv.org/abs/2301.13141