Zobrazeno 1 - 10
of 10
pro vyhledávání: '"S. Gene Kim"'
Autor:
Karl Kiser, Jin Zhang, Ayesha Bharadwaj Das, James A. Tranos, Youssef Zaim Wadghiri, S. Gene Kim
This manuscript aims to evaluate the robustness and significance of the intracellular water lifetime (τi) parameter estimated using the two flip-angle Dynamic Contrast-Enhanced (DCE) MRI approach. The repeatability of contrast kinetic parameter meas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97ff92bf180600ef3cd40711c2a4574a
https://doi.org/10.21203/rs.3.rs-1816480/v1
https://doi.org/10.21203/rs.3.rs-1816480/v1
Autor:
S. Gene Kim, Michele B. Drotman
Publikováno v:
Advances in Magnetic Resonance Technology and Applications ISBN: 9780128227299
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76b06eaaceeae0d07ad429b4b0856685
https://doi.org/10.1016/b978-0-12-822729-9.00006-0
https://doi.org/10.1016/b978-0-12-822729-9.00006-0
Autor:
Linda Moy, Taro Makino, Kyunghyun Cho, Yiqiu Shen, Laura Heacock, Zhe Huang, Nan Wu, Jason Phang, Jungkyu Park, S. Gene Kim, Krzysztof J. Geras
Publikováno v:
J Digit Imaging
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we buil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6cdce670a993d586ee25810b45504d0
https://europepmc.org/articles/PMC8669066/
https://europepmc.org/articles/PMC8669066/
Publikováno v:
Eur J Radiol
Purpose To assess the association of fatty acid levels in mammary adipose tissue of postmenopausal women with the presence of breast cancer using the Gradient-echo Spectroscopic Imaging (GSI). Materials and methods Unilateral GSI was performed at 3 T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28a609e5d27f1821c45215fcafd05203
https://europepmc.org/articles/PMC6568320/
https://europepmc.org/articles/PMC6568320/
Autor:
Yiqiu Shen, Jason Phang, Nan Wu, S. Gene Kim, Krzysztof J. Geras, Linda Moy, Jungkyu Park, Kyunghyun Cho
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030326913
MLMI@MICCAI
MLMI@MICCAI
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and sma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbeeecacdebcbfa292c571b47240b9f6
https://doi.org/10.1007/978-3-030-32692-0_3
https://doi.org/10.1007/978-3-030-32692-0_3
Autor:
Naziya Samreen, Beatriu Reig, Kara Ho, Kyunghyun Cho, Jungkyu Park, Laura Heacock, Zhe Huang, Sushma Gaddam, Eric Kim, Yiming Gao, Linda Moy, Joshua D. Weinstein, Jason Phang, Nan Wu, Jiyon Lee, Yiqiu Shen, Alana A. Lewin, Masha Zorin, Ujas Parikh, Krzysztof J. Geras, S. Gene Kim, Krystal Airola, Stacey Wolfson, Hildegard B. Toth, Stephanie H Chung, Joe Katsnelson, Thibault Févry, Eralda Mema, Leng Leng Young Lin, Kristine Pysarenko, Esther Hwang, Stanisław Jastrzębski
Publikováno v:
IEEE transactions on medical imaging
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53329e18a25d244b7312111ada30eecd
Autor:
Yiqiu Shen, Krzysztof J. Geras, Linda Moy, Eric Kim, Jingyi Su, Stacey Wolfson, Kyunghyun Cho, Nan Wu, S. Gene Kim
Publikováno v:
ICASSP
Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9881d48590c8253e0cbbb3000013182a
http://arxiv.org/abs/1711.03674
http://arxiv.org/abs/1711.03674
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