Zobrazeno 1 - 10
of 883
pro vyhledávání: '"A, Gutman David"'
This paper develops an adaptive Proximal Alternating Direction Method of Multipliers (P-ADMM) for solving linearly-constrained, weakly convex, composite optimization problems. This method is adaptive to all problem parameters, including smoothness an
Externí odkaz:
http://arxiv.org/abs/2407.09927
This work provides the first convergence analysis for the Randomized Block Coordinate Descent method for minimizing a function that is both H\"older smooth and block H\"older smooth. Our analysis applies to objective functions that are non-convex, co
Externí odkaz:
http://arxiv.org/abs/2403.08080
This paper introduces a quantum-classical hybrid algorithm for generalized pattern search (GPS) algorithms. We introduce a quantum search step algorithm using amplitude amplification, which reduces the number of oracle calls needed during the search
Externí odkaz:
http://arxiv.org/abs/2305.01703
Autor:
Clunie, David A., Flanders, Adam, Taylor, Adam, Erickson, Brad, Bialecki, Brian, Brundage, David, Gutman, David, Prior, Fred, Seibert, J Anthony, Perry, John, Gichoya, Judy Wawira, Kirby, Justin, Andriole, Katherine, Geneslaw, Luke, Moore, Steve, Fitzgerald, TJ, Tellis, Wyatt, Xiao, Ying, Farahani, Keyvan
This report addresses the technical aspects of de-identification of medical images of human subjects and biospecimens, such that re-identification risk of ethical, moral, and legal concern is sufficiently reduced to allow unrestricted public sharing
Externí odkaz:
http://arxiv.org/abs/2303.10473
Autor:
Purkayastha, Subhanik, Shalu, Hrithwik, Gutman, David, Modak, Shakeel, Basu, Ellen, Kushner, Brian, Kramer, Kim, Haque, Sofia, Stember, Joseph
Artificial intelligence (AI) in radiology has made great strides in recent years, but many hurdles remain. Overfitting and lack of generalizability represent important ongoing challenges hindering accurate and dependable clinical deployment. If AI al
Externí odkaz:
http://arxiv.org/abs/2211.14499
This paper expands the Cyclic Block Proximal Gradient method for block separable composite minimization by allowing for inexactly computed gradients and proximal maps. The resultant algorithm, the Inexact Cyclic Block Proximal Gradient (I-CBPG) metho
Externí odkaz:
http://arxiv.org/abs/2201.00896
Autor:
Schapiro, Denis, Yapp, Clarence, Sokolov, Artem, Reynolds, Sheila M., Chen, Yu-An, Sudar, Damir, Xie, Yubin, Muhlich, Jeremy L., Arias-Camison, Raquel, Arena, Sarah, Taylor, Adam J., Nikolov, Milen, Tyler, Madison, Lin, Jia-Ren, Burlingame, Erik A., Network, Human Tumor Atlas, Chang, Young H., Farhi, Samouil L, Thorsson, Vésteinn, Venkatamohan, Nithya, Drewes, Julia L., Pe'er, Dana, Gutman, David A., Herrmann, Markus D., Gehlenborg, Nils, Bankhead, Peter, Roland, Joseph T., Herndon, John M., Snyder, Michael P., Angelo, Michael, Nolan, Garry, Swedlow, Jason R., Schultz, Nikolaus, Merrick, Daniel T., Mazzilli, Sarah A., Cerami, Ethan, Rodig, Scott J., Santagata, Sandro, Sorger, Peter K.
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation
Externí odkaz:
http://arxiv.org/abs/2108.09499
Autor:
Moghanaki, Drew, Taylor, James, Bryant, Alex K., Vitzthum, Lucas K., Sebastian, Nikhil, Gutman, David, Burns, Abigail, Huang, Zhonglu, Lewis, Jennifer A., Spalluto, Lucy B., Williams, Christina D., Sullivan, Donald R., Slatore, Christopher G., Behera, Madhusmita, Stokes, William A.
Publikováno v:
In Clinical Lung Cancer May 2024 25(3):225-232
Autor:
Amgad, Mohamed, Atteya, Lamees A., Hussein, Hagar, Mohammed, Kareem Hosny, Hafiz, Ehab, Elsebaie, Maha A. T., Alhusseiny, Ahmed M., AlMoslemany, Mohamed Atef, Elmatboly, Abdelmagid M., Pappalardo, Philip A., Sakr, Rokia Adel, Mobadersany, Pooya, Rachid, Ahmad, Saad, Anas M., Alkashash, Ahmad M., Ruhban, Inas A., Alrefai, Anas, Elgazar, Nada M., Abdulkarim, Ali, Farag, Abo-Alela, Etman, Amira, Elsaeed, Ahmed G., Alagha, Yahya, Amer, Yomna A., Raslan, Ahmed M., Nadim, Menatalla K., Elsebaie, Mai A. T., Ayad, Ahmed, Hanna, Liza E., Gadallah, Ahmed, Elkady, Mohamed, Drumheller, Bradley, Jaye, David, Manthey, David, Gutman, David A., Elfandy, Habiba, Cooper, Lee A. D.
Publikováno v:
GigaScience, 11 (2022)
High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled insta
Externí odkaz:
http://arxiv.org/abs/2102.09099
Autor:
Liopyris, Konstantinos, Navarrete-Dechent, Cristian, Marchetti, Michael A., Rotemberg, Veronica, Apalla, Zoe, Argenziano, Giuseppe, Blum, Andreas, Braun, Ralph P., Carrera, Cristina, Codella, Noel C.F., Combalia, Marc, Dusza, Stephen W., Gutman, David A., Helba, Brian, Hofmann-Wellenhof, Rainer, Jaimes, Natalia, Kittler, Harald, Kose, Kivanc, Lallas, Aimilios, Longo, Caterina, Malvehy, Josep, Menzies, Scott, Nelson, Kelly C., Paoli, John, Puig, Susana, Rabinovitz, Harold S., Rishpon, Ayelet, Russo, Teresa, Scope, Alon, Soyer, H. Peter, Stein, Jennifer A., Stolz, Willhelm, Sgouros, Dimitrios, Stratigos, Alexander J., Swanson, David L., Thomas, Luc, Tschandl, Philipp, Zalaudek, Iris, Weber, Jochen, Halpern, Allan C., Marghoob, Ashfaq A.
Publikováno v:
In Journal of Investigative Dermatology March 2024 144(3):531-539