Zobrazeno 1 - 10
of 728
pro vyhledávání: '"Moy, Linda"'
Autor:
Solomon, Eddy, Johnson, Patricia M., Tan, Zhengguo, Tibrewala, Radhika, Lui, Yvonne W., Knoll, Florian, Moy, Linda, Kim, Sungheon Gene, Heacock, Laura
This data curation work introduces the first large-scale dataset of radial k-space and DICOM data for breast DCE-MRI acquired in diagnostic breast MRI exams. Our dataset includes case-level labels indicating patient age, menopause status, lesion stat
Externí odkaz:
http://arxiv.org/abs/2406.05270
Autor:
Maleki, Farhad, Moy, Linda, Forghani, Reza, Ghosh, Tapotosh, Ovens, Katie, Langer, Steve, Rouzrokh, Pouria, Khosravi, Bardia, Ganjizadeh, Ali, Warren, Daniel, Daneshjou, Roxana, Moassefi, Mana, Avval, Atlas Haddadi, Sotardi, Susan, Tenenholtz, Neil, Kitamura, Felipe, Kline, Timothy
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and
Externí odkaz:
http://arxiv.org/abs/2401.08847
Autor:
Park, Jungkyu, Chłędowski, Jakub, Jastrzębski, Stanisław, Witowski, Jan, Xu, Yanqi, Du, Linda, Gaddam, Sushma, Kim, Eric, Lewin, Alana, Parikh, Ujas, Plaunova, Anastasia, Chen, Sardius, Millet, Alexandra, Park, James, Pysarenko, Kristine, Patel, Shalin, Goldberg, Julia, Wegener, Melanie, Moy, Linda, Heacock, Laura, Reig, Beatriu, Geras, Krzysztof J.
3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be t
Externí odkaz:
http://arxiv.org/abs/2210.08645
Autor:
Kline, Timothy L., Kitamura, Felipe, Pan, Ian, Korchi, Amine M., Tenenholtz, Neil, Moy, Linda, Gichoya, Judy Wawira, Santos, Igor, Blumer, Steven, Hwang, Misha Ysabel, Git, Kim-Ann, Shroff, Abishek, Walach, Elad, Shih, George, Langer, Steve
With the recent advances in A.I. methodologies and their application to medical imaging, there has been an explosion of related research programs utilizing these techniques to produce state-of-the-art classification performance. Ultimately, these res
Externí odkaz:
http://arxiv.org/abs/2202.01863
Autor:
Makino, Taro, Jastrzebski, Stanislaw, Oleszkiewicz, Witold, Chacko, Celin, Ehrenpreis, Robin, Samreen, Naziya, Chhor, Chloe, Kim, Eric, Lee, Jiyon, Pysarenko, Kristine, Reig, Beatriu, Toth, Hildegard, Awal, Divya, Du, Linda, Kim, Alice, Park, James, Sodickson, Daniel K., Heacock, Laura, Moy, Linda, Cho, Kyunghyun, Geras, Krzysztof J.
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which human perception remains invariant. If we can better understand the dif
Externí odkaz:
http://arxiv.org/abs/2011.14036
Autor:
Wu, Nan, Huang, Zhe, Shen, Yiqiu, Park, Jungkyu, Phang, Jason, Makino, Taro, Kim, S. Gene, Cho, Kyunghyun, Heacock, Laura, Moy, Linda, Geras, Krzysztof J.
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:
http://arxiv.org/abs/2009.09282
Autor:
Oleszkiewicz, Witold, Makino, Taro, Jastrzębski, Stanisław, Trzciński, Tomasz, Moy, Linda, Cho, Kyunghyun, Heacock, Laura, Geras, Krzysztof J.
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of
Externí odkaz:
http://arxiv.org/abs/2003.10041
Autor:
Shen, Yiqiu, Wu, Nan, Phang, Jason, Park, Jungkyu, Liu, Kangning, Tyagi, Sudarshini, Heacock, Laura, Kim, S. Gene, Moy, Linda, Cho, Kyunghyun, Geras, Krzysztof J.
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analy
Externí odkaz:
http://arxiv.org/abs/2002.07613
Publikováno v:
In PET Clinics October 2023 18(4):567-575
Autor:
Lewin, John, Schoenherr, Sven, Seebass, Martin, Lin, MingDe, Philpotts, Liane, Etesami, Maryam, Butler, Reni, Durand, Melissa, Heller, Samantha, Heacock, Laura, Moy, Linda, Tocino, Irena, Westerhoff, Malte
Publikováno v:
In Clinical Imaging September 2023 101:200-205