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pro vyhledávání: '"Lu, Michael"'
We consider (stochastic) softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). While the PG objective is non-concave, recent research has used the objective's smoothness and gradient domination properties to a
Externí odkaz:
http://arxiv.org/abs/2405.13136
Brain tumor segmentation presents a formidable challenge in the field of Medical Image Segmentation. While deep-learning models have been useful, human expert segmentation remains the most accurate method. The recently released Segment Anything Model
Externí odkaz:
http://arxiv.org/abs/2310.06162
Autor:
Lu, Michael
Ciliates are important model organisms that have been used to study many aspects of cellular biology, including telomeres, histone modifications, and ribozymes. These unicellular eukaryotes house both a germline genome and a somatic genome in distinc
Deep learning is the state-of-the-art for medical imaging tasks, but requires large, labeled datasets. For risk prediction, large datasets are rare since they require both imaging and follow-up (e.g., diagnosis codes). However, the release of publicl
Externí odkaz:
http://arxiv.org/abs/2306.08955
Autor:
Dogan, Mustafa Doga, Taka, Ahmad, Lu, Michael, Zhu, Yunyi, Kumar, Akshat, Gupta, Aakar, Mueller, Stefanie
Existing approaches for embedding unobtrusive tags inside 3D objects require either complex fabrication or high-cost imaging equipment. We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed a
Externí odkaz:
http://arxiv.org/abs/2202.06165
Autor:
Zhang, Zhongyi, Weiss, Jakob, Taron, Jana, Zeleznik, Roman, Lu, Michael T., Aerts, Hugo J. W. L.
Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced ches
Externí odkaz:
http://arxiv.org/abs/2202.02377
Autor:
Karady, Julia, Lu, Michael T., Bergström, Göran, Mayrhofer, Thomas, Taron, Jana, Foldyna, Borek, Paradis, Kayla, McCallum, Sara, Aberg, Judith A., Currier, Judith S., Fitch, Kathleen V., Fulda, Evelynne S., Bloomfield, Gerald S., Overton, Edgar T., Lind, Lars, Östgren, Carl Johan, Elvstam, Olof, Söderberg, Stefan, Jernberg, Tomas, Pepe, Rosalie, Dubé, Michael P., Mushatt, David, Fichtenbaum, Carl J., Malvestutto, Carlos, Zanni, Markella V., Hoffmann, Udo, Ribaudo, Heather, Grinspoon, Steven K., Douglas, Pamela S.
Publikováno v:
In JACC: Advances June 2024 3(6)
Autor:
Karady, Julia, McGarrah, Robert W, Nguyen, Maggie, Giamberardino, Stephanie N, Meyersohn, Nandini, Lu, Michael T, Staziaki, Pedro V, Puchner, Stefan B, Bittner, Daniel O, Foldyna, Borek, Mayrhofer, Thomas, Connelly, Margery A, Tchernof, Andre, White, Phillip J, Nasir, Khurram, Corey, Kathleen, Voora, Deepak, Pagidipati, Neha, Ginsburg, Geoffrey S, Kraus, William E, Hoffmann, Udo, Douglas, Pamela S, Shah, Svati H, Ferencik, Maros
Publikováno v:
In American Journal of Preventive Cardiology June 2024 18
In recent years, convolutional neural networks (CNNs) have been successfully implemented to various image recognition applications, such as medical image analysis, object detection, and image segmentation. Many studies and applications have been work
Externí odkaz:
http://arxiv.org/abs/2105.01840
Autor:
Bhattacharya, Romit, Uddin, Md Mesbah, Patel, Aniruddh P., Niroula, Abhishek, Finneran, Phoebe, Bernardo, Rachel, Fitch, Kathleen V., Lu, Michael T., Bloomfield, Gerald S., Malvestutto, Carlos, Aberg, Judy A., Fichtenbaum, Carl J., Hornsby, Whitney, Ribaudo, Heather J., Libby, Peter, Ebert, Benjamin L., Zanni, Markella V., Douglas, Pamela S., Grinspoon, Steven K., Natarajan, Pradeep
Publikováno v:
In Blood Advances 27 February 2024 8(4):959-967