Zobrazeno 1 - 10
of 210
pro vyhledávání: '"Park, Jungkyu"'
Full Field Digital Mammograms (FFDMs) and Digital Breast Tomosynthesis (DBT) are the two most widely used imaging modalities for breast cancer screening. Although DBT has increased cancer detection compared to FFDM, its widespread adoption in clinica
Externí odkaz:
http://arxiv.org/abs/2408.08560
Autor:
Shen, Yiqiu, Park, Jungkyu, Yeung, Frank, Goldberg, Eliana, Heacock, Laura, Shamout, Farah, Geras, Krzysztof J.
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities to integrate
Externí odkaz:
http://arxiv.org/abs/2311.03217
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:
Moon, Eun Young1 (AUTHOR) m.eunyoung@gmail.com, Park, Jungkyu2 (AUTHOR) jkp@knu.ac.kr, Ka, Yohan1 (AUTHOR) yka@handong.edu
Publikováno v:
Journal of Career Development. Oct2024, Vol. 51 Issue 5, p527-543. 17p.
Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability. A common approach to creating saliency maps involves generating input masks that mask out portions of an image to maximally
Externí odkaz:
http://arxiv.org/abs/2010.09750
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:
Shamout, Farah E., Shen, Yiqiu, Wu, Nan, Kaku, Aakash, Park, Jungkyu, Makino, Taro, Jastrzębski, Stanisław, Witowski, Jan, Wang, Duo, Zhang, Ben, Dogra, Siddhant, Cao, Meng, Razavian, Narges, Kudlowitz, David, Azour, Lea, Moore, William, Lui, Yvonne W., Aphinyanaphongs, Yindalon, Fernandez-Granda, Carlos, Geras, Krzysztof J.
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a
Externí odkaz:
http://arxiv.org/abs/2008.01774
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
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