Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Koohbanani, Navid Alemi"'
Autor:
Jiao, Yiping, van der Laak, Jeroen, Albarqouni, Shadi, Li, Zhang, Tan, Tao, Bhalerao, Abhir, Ma, Jiabo, Sun, Jiamei, Pocock, Johnathan, Pluim, Josien P. W., Koohbanani, Navid Alemi, Bashir, Raja Muhammad Saad, Raza, Shan E Ahmed, Liu, Sibo, Graham, Simon, Wetstein, Suzanne, Khurram, Syed Ali, Watson, Thomas, Rajpoot, Nasir, Veta, Mitko, Ciompi, Francesco
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells,
Externí odkaz:
http://arxiv.org/abs/2301.06304
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating d
Externí odkaz:
http://arxiv.org/abs/2108.13368
Autor:
Koohbanani, Navid Alemi, Unnikrishnan, Balagopal, Khurram, Syed Ali, Krishnaswamy, Pavitra, Rajpoot, Nasir
While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In this paper, we propose a self-supervised CNN approach to leverage unlabel
Externí odkaz:
http://arxiv.org/abs/2008.05571
Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because
Externí odkaz:
http://arxiv.org/abs/2005.14511
Autor:
Gamper, Jevgenij, Koohbanani, Navid Alemi, Benes, Ksenija, Graham, Simon, Jahanifar, Mostafa, Khurram, Syed Ali, Azam, Ayesha, Hewitt, Katherine, Rajpoot, Nasir
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides. However, it is impe
Externí odkaz:
http://arxiv.org/abs/2003.10778
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, pro
Externí odkaz:
http://arxiv.org/abs/1909.03253
Autor:
Zhou, Yanning, Graham, Simon, Koohbanani, Navid Alemi, Shaban, Muhammad, Heng, Pheng-Ann, Rajpoot, Nasir
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along wi
Externí odkaz:
http://arxiv.org/abs/1909.01068
Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to se
Externí odkaz:
http://arxiv.org/abs/1908.10356
Autor:
Bagheri, Amir Behzad, Rouzi, Mohammad Dehghan, Koohbanani, Navid Alemi, Mahoor, Mohammad H., Finco, M.G., Lee, Myeounggon, Najafi, Bijan, Chung, Jayer
Publikováno v:
In Seminars in Vascular Surgery
Autor:
Vu, Quoc Dang, Graham, Simon, To, Minh Nguyen Nhat, Shaban, Muhammad, Qaiser, Talha, Koohbanani, Navid Alemi, Khurram, Syed Ali, Kurc, Tahsin, Farahani, Keyvan, Zhao, Tianhao, Gupta, Rajarsi, Kwak, Jin Tae, Rajpoot, Nasir, Saltz, Joel
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. S
Externí odkaz:
http://arxiv.org/abs/1810.13230