Zobrazeno 1 - 10
of 330
pro vyhledávání: '"Bashashati, Ali"'
Autor:
Fradkin, Philip, Azadi, Puria, Suri, Karush, Wenkel, Frederik, Bashashati, Ali, Sypetkowski, Maciej, Beaini, Dominique
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering m
Externí odkaz:
http://arxiv.org/abs/2409.08302
Bevacizumab is a widely studied targeted therapeutic drug used in conjunction with standard chemotherapy for the treatment of recurrent ovarian cancer. While its administration has shown to increase the progression-free survival (PFS) in patients wit
Externí odkaz:
http://arxiv.org/abs/2407.20596
Autor:
Mirabadi, Ali Khajegili, Archibald, Graham, Darbandsari, Amirali, Contreras-Sanz, Alberto, Nakhli, Ramin Ebrahim, Asadi, Maryam, Zhang, Allen, Gilks, C. Blake, Black, Peter, Wang, Gang, Farahani, Hossein, Bashashati, Ali
Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take adv
Externí odkaz:
http://arxiv.org/abs/2402.03592
Autor:
Nakhli, Ramin, Zhang, Allen, Farahani, Hossein, Darbandsari, Amirali, Shenasa, Elahe, Thiessen, Sidney, Milne, Katy, McAlpine, Jessica, Nelson, Brad, Gilks, C Blake, Bashashati, Ali
In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells
Externí odkaz:
http://arxiv.org/abs/2303.04696
Autor:
Nakhli, Ramin, Moghadam, Puria Azadi, Mi, Haoyang, Farahani, Hossein, Baras, Alexander, Gilks, Blake, Bashashati, Ali
Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further
Externí odkaz:
http://arxiv.org/abs/2303.00865
Autor:
Bazargani, Roozbeh, Fazli, Ladan, Goldenberg, Larry, Gleave, Martin, Bashashati, Ali, Salcudean, Septimiu
Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with late fusion. In order to leverage the multi-magn
Externí odkaz:
http://arxiv.org/abs/2212.08781
Autor:
Moghadam, Puria Azadi, Van Dalen, Sanne, Martin, Karina C., Lennerz, Jochen, Yip, Stephen, Farahani, Hossein, Bashashati, Ali
Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification o
Externí odkaz:
http://arxiv.org/abs/2209.13167
Cell identification within the H&E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep lear
Externí odkaz:
http://arxiv.org/abs/2208.06445
Autor:
Bazargani, Roozbeh, Fazli, Ladan, Gleave, Martin, Goldenberg, Larry, Bashashati, Ali, Salcudean, Septimiu
Publikováno v:
In Medical Image Analysis August 2024 96
Autor:
Wang, Ching-Wei, Firdi, Nabila Puspita, Chu, Tzu-Chiao, Faiz, Mohammad Faiz Iqbal, Iqbal, Mohammad Zafar, Li, Yifan, Yang, Bo, Mallya, Mayur, Bashashati, Ali, Li, Fei, Wang, Haipeng, Lu, Mengkang, Xia, Yong, Chao, Tai-Kuang
Publikováno v:
In Medical Image Analysis January 2025 99