Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Aïcha BenTaieb"'
Autor:
Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau, Kshitij Ingale, Yoni Muller, Rachel Baits, Sebastian Pretzer, Aïcha BenTaieb, Abigail Gordhamer, Roberto Nussenzveig, Adam Cole, Matthew O. Leavitt, Ryan D. Jones, Rohan P. Joshi, Nike Beaubier, Martin C. Stumpe, Kunal Nagpal
Publikováno v:
npj Precision Oncology, Vol 8, Iss 1, Pp 1-10 (2024)
Abstract Microsatellite instability-high (MSI-H) is a tumor-agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction
Externí odkaz:
https://doaj.org/article/7805a2768fad474dabc5522838e58ff9
Autor:
Bolesław L. Osinski, Aïcha BenTaieb, Irvin Ho, Ryan D. Jones, Rohan P. Joshi, Andrew Westley, Michael Carlson, Caleb Willis, Luke Schleicher, Brett M. Mahon, Martin C. Stumpe
Publikováno v:
Modern Pathology. 35:1791-1803
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40210d2e3b8d8e3ec6cf00b664391852
https://doi.org/10.1038/s41379-022-01161-0
https://doi.org/10.1038/s41379-022-01161-0
Publikováno v:
Computerized Medical Imaging and Graphics. 70:111-118
PET imaging captures the metabolic activity of tissues and is commonly visually interpreted by clinicians for detecting cancer, assessing tumor progression, and evaluating response to treatment. To automate accomplishing these tasks, it is important
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a660e202a41179700731a9e2d77160e
https://doi.org/10.1016/j.compmedimag.2018.09.008
https://doi.org/10.1016/j.compmedimag.2018.09.008
Autor:
Theodore H. Welling, Diane M. Simeone, Benjamin A. Krantz, Aïcha BenTaieb, Andrea Cancino, Chi Sing Ho, Jeffrey A. Borgia, Jeremy V. Mathews, Brandon Mapes, Michael A. Streit, Jian Jun Wei, Ameen A. Salahudeen, Bridgette E. Drummond, Veronica Sanchez-Freire, Martin C. Stumpe, Aly A. Khan, Tim A. Rand, Ende Zhao, Kelly E. McKinnon, Benjamin D. Leibowitz, Demirkan B. Gursel, Madhavi Kannan, Jagadish Venkataraman, Yi-Hung Carol Tan, Brian M. Larsen, Jonathan R. Dry, Gaurav Khullar, Jenna M. Shaxted, Catherine Igartua, Kevin P. White, Daniel V.T. Catenacci, Ashiq Masood, Jason Perera, Jessica Metti, Michelle M. Stein, Igor Dolgalev, Lee F. Langer, Yilin Zhang
Publikováno v:
Cell Reports, Vol 36, Iss 4, Pp 109429-(2021)
Summary: Patient-derived tumor organoids (TOs) are emerging as high-fidelity models to study cancer biology and develop novel precision medicine therapeutics. However, utilizing TOs for systems-biology-based approaches has been limited by a lack of s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85ee6070df11c2b91a956a81ab264980
https://pubmed.ncbi.nlm.nih.gov/34320344
https://pubmed.ncbi.nlm.nih.gov/34320344
Autor:
Aïcha BenTaieb, Ghassan Hamarneh
Publikováno v:
IEEE Transactions on Medical Imaging. 37:792-802
It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28a3e04f536f89d7fbd08f0e58bcb232
https://doi.org/10.1109/tmi.2017.2781228
https://doi.org/10.1109/tmi.2017.2781228
Publikováno v:
Medical Image Analysis. 39:194-205
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95ed69a8440434c94ecf66e1317d088a
https://doi.org/10.1016/j.media.2017.04.008
https://doi.org/10.1016/j.media.2017.04.008
Publikováno v:
Medical Imaging: Image Processing
The effort involved in creating accurate ground truth segmentation maps hinders advances in machine learning approaches to tumor delineation in clinical positron emission tomography (PET) scans. To address this challenge, we propose a fully convoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e7095a572b3ba1e7a2297727a1eb74f
https://doi.org/10.1117/12.2512274
https://doi.org/10.1117/12.2512274
Autor:
Yefeng Zheng, Puneet Sharma, S. Kevin Zhou, Dorin Comaniciu, Saeid Asgari Taghanaki, Ghassan Hamarneh, Bogdan Georgescu, Zhoubing Xu, Aïcha BenTaieb, Anmol Sharma
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030326913
MLMI@MICCAI
MLMI@MICCAI
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures and reducing the risks for vanishing gradients. The skip connections equip encoder-decoder l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ec83eca5cf75a83ed8b2c5fe98a1a8e
https://doi.org/10.1007/978-3-030-32692-0_48
https://doi.org/10.1007/978-3-030-32692-0_48
Autor:
Aïcha BenTaieb, Farid Golnaraghi, Majid Shokoufi, Ghassan Hamarneh, Amir Zahiremami, Hanene Ben Yedder
Publikováno v:
Machine Learning for Medical Image Reconstruction ISBN: 9783030001285
MLMIR@MICCAI
MLMIR@MICCAI
Diffuse optical tomography (DOT) is a relatively new imaging modality that has demonstrated its clinical potential of probing tumors in a non-invasive and affordable way. Image reconstruction is an ill-posed challenging task because knowledge of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24347c8d4e469bec309519c25cabab79
https://doi.org/10.1007/978-3-030-00129-2_13
https://doi.org/10.1007/978-3-030-00129-2_13
Autor:
Aïcha BenTaieb, Ghassan Hamarneh
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009335
MICCAI (2)
MICCAI (2)
Automatically recognizing cancers from multi-gigapixel whole slide histopathology images is one of the challenges facing machine and deep learning based solutions for digital pathology. Currently, most automatic systems for histopathology are not sca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6cd2e161cbfead72c5ba84ffda5c59b
https://doi.org/10.1007/978-3-030-00934-2_15
https://doi.org/10.1007/978-3-030-00934-2_15