Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Mishka Gidwani"'
Autor:
Bin Lou, PhD, Semihcan Doken, BA, Tingliang Zhuang, PhD, Danielle Wingerter, BE, Mishka Gidwani, BS, Nilesh Mistry, PhD, Lance Ladic, PhD, Ali Kamen, PhD, Mohamed E Abazeed, MD
Publikováno v:
The Lancet: Digital Health, Vol 1, Iss 3, Pp e136-e147 (2019)
Summary: Background: Radiotherapy continues to be delivered without consideration of individual tumour characteristics. To advance towards more precise treatments in radiotherapy, we queried the lung CT-derived feature space to identify radiation sen
Externí odkaz:
https://doaj.org/article/6432760552d346d2b0cc40b8d802c301
Autor:
Yue Geng, Siddarth Chandrasekaran, Jong-Wei Hsu, Mishka Gidwani, Andrew D Hughes, Michael R King
Publikováno v:
PLoS ONE, Vol 8, Iss 1, p e54959 (2013)
Hematogeneous metastasis can occur via a cascade of circulating tumor cell adhesion events to the endothelial lining of the vasculature, i.e. the metastatic cascade. Interestingly, the pro-inflammatory cytokines IL-6 and TNF-α, which play an importa
Externí odkaz:
https://doaj.org/article/4d25a26b12664c6cb0fe238d684d8291
Autor:
Mishka Gidwani, Ken Chang, Jay Biren Patel, Katharina Viktoria Hoebel, Syed Rakin Ahmed, Praveer Singh, Clifton David Fuller, Jayashree Kalpathy-Cramer
Publikováno v:
Radiology. 307
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published
Autor:
Michael F. Chiang, Ashwin Vaswani, Mehak Aggarwal, John Campbell, Jimmy S. Chen, Katharina Hoebel, Praveer Singh, Jayashree Kalpathy-Cramer, Nishanth Thumbavanam Arun, Ken Chang, Vibha Agarwal, Liangqiong Qu, Christopher P. Bridge, Daniel L. Rubin, Sharut Gupta, R. V. Paul Chan, Charles Lu, Mishka Gidwani, Jay M. Patel, Shruti Raghavan
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one dataset may suffer a significant decline in performance when tested at on different datasets. While poolin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3d151dbb0728719c03ee016e117702b
https://doi.org/10.21203/rs.3.rs-1087025/v1
https://doi.org/10.21203/rs.3.rs-1087025/v1
Autor:
Mishka Gidwani, Ken Chang, Jay Biren Patel, Albert Eusik Kim, Elizabeth R. Gerstner, Raymond Yi-kun Huang, Jayashree Kalpathy-Cramer
Publikováno v:
Journal of Clinical Oncology. 40:e14003-e14003
e14003 Background: The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) is the standard for therapeutic response assessment in brain metastases (BM) patients. The criteria relies on a human reader to annotate target (≥10 mm) and
Publikováno v:
Auto-Segmentation for Radiation Oncology
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7baae1a21034f91c93e9138266197681
https://doi.org/10.1201/9780429323782-17
https://doi.org/10.1201/9780429323782-17
Autor:
Nishanth Thumbavanam Arun, Katharina Hoebel, Praveer Singh, Mishka Gidwani, Elizabeth R. Gerstner, Jay B. Patel, Sharut Gupta, Ken Chang, Mehak Aggarwal, Bruce R. Rosen, Jayashree Kalpathy-Cramer
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030720865
BrainLes@MICCAI (2)
BrainLes@MICCAI (2)
Segmentation of gliomas into distinct sub-regions can help guide clinicians in tasks such as surgical planning, prognosis, and treatment response assessment. Manual delineation is time-consuming and prone to inter-rater variability. In this work, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3ee7fd963a4ebda71f3a4e1de86f35f
https://doi.org/10.1007/978-3-030-72087-2_20
https://doi.org/10.1007/978-3-030-72087-2_20
Autor:
Nishanth Thumbavanam Arun, Bryan Chen, Mehak Aggarwal, Ken Chang, Katharina Hoebel, Sharut Gupta, Matthew D. Li, Praveer Singh, Nathan Gaw, Jayashree Kalpathy-Cramer, Julius Adebayo, Mishka Gidwani, Jay B. Patel
Publikováno v:
Radiol Artif Intell
PURPOSE: To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging. MATERIALS AND METHODS: Using two large publicly available radiology datasets (Society for Imaging Informatics in Medicine–American College of
Autor:
Mishka Gidwani, Nathan Gaw, Jayashree Kalpathy-Cramer, Julius Adebayo, Sharut Gupta, Katharina Hoebel, Matthew D. Li, Ken Chang, Mehak Aggarwal, Praveer Singh, Bryan Chen, Nishanth Thumbavanam Arun, Jay B. Patel
Saliency maps have become a widely used method to make deep learning models more interpretable by providing post-hoc explanations of classifiers through identification of the most pertinent areas of the input medical image. They are increasingly bein
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8612b86f604517dec04f95d4b45d506e
https://doi.org/10.1101/2020.07.28.20163899
https://doi.org/10.1101/2020.07.28.20163899
Autor:
Nishaeminth Thumbavanam Arun, Brent P. Little, Praveer Singh, Francis Deng, Susanna I. Lee, Jayashree Kalpathy-Cramer, Mishka Gidwani, Anushri Parakh, Dexter P. Mendoza, Ken Chang, Aileen O'Shea, Min Lang, Matthew D. Li
Publikováno v:
Radiology. Artificial Intelligence
medRxiv
medRxiv
Purpose To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. Materials and Methods A convolutional Siamese neural network-based algorithm was tra