Zobrazeno 1 - 10
of 1 268
pro vyhledávání: '"JONES, CRAIG A."'
Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whether other observable galaxy features, such as their morphologies or larg
Externí odkaz:
http://arxiv.org/abs/2407.13735
Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficulties in interpretability and the 'black box' issue. To bridge this gap
Externí odkaz:
http://arxiv.org/abs/2404.10031
Polyp segmentation is a key aspect of colorectal cancer prevention, enabling early detection and guiding subsequent treatments. Intelligent diagnostic tools, including deep learning solutions, are widely explored to streamline and potentially automat
Externí odkaz:
http://arxiv.org/abs/2401.11671
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct quantificatio
Externí odkaz:
http://arxiv.org/abs/2311.11367
Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess training in the absence of a hold-out validation or test set. Materials an
Externí odkaz:
http://arxiv.org/abs/2309.14591
Autor:
Feng, Anqi, Johnson, Dimitri, Reilly, Grace R., Thangamathesvaran, Loka, Nampomba, Ann, Unberath, Mathias, Scott, Adrienne W., Jones, Craig
Importance: Ultra-widefield fundus photography (UWF-FP) has shown utility in sickle cell retinopathy screening; however, image artifact may diminish quality and gradeability of images. Objective: To create an automated algorithm for UWF-FP artifact c
Externí odkaz:
http://arxiv.org/abs/2307.05780
Autor:
Latheef, Ammar Ahmed Pallikonda, Ghate, Sejal, Hui, Zhipeng, Santamaria-Pang, Alberto, Tarapov, Ivan, Sair, Haris I, Jones, Craig K
Resting State Networks (RSNs) of the brain extracted from Resting State functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical planning to guide the neurosurgeon. This is difficult, though, as expert knowledge is required to lab
Externí odkaz:
http://arxiv.org/abs/2305.03814
Autor:
Kim, Daniel D, Chandra, Rajat S, Peng, Jian, Wu, Jing, Feng, Xue, Atalay, Michael, Bettegowda, Chetan, Jones, Craig, Sair, Haris, Liao, Wei-hua, Zhu, Chengzhang, Zou, Beiji, Yang, Li, Kazerooni, Anahita Fathi, Nabavizadeh, Ali, Bai, Harrison X, Jiao, Zhicheng
Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active learning (AL) addresses this by training a model on a subset of the most
Externí odkaz:
http://arxiv.org/abs/2302.10185
Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functio
Externí odkaz:
http://arxiv.org/abs/2209.08200
Autor:
The Astropy Collaboration, Price-Whelan, Adrian M., Lim, Pey Lian, Earl, Nicholas, Starkman, Nathaniel, Bradley, Larry, Shupe, David L., Patil, Aarya A., Corrales, Lia, Brasseur, C. E., Nöthe, Maximilian, Donath, Axel, Tollerud, Erik, Morris, Brett M., Ginsburg, Adam, Vaher, Eero, Weaver, Benjamin A., Tocknell, James, Jamieson, William, van Kerkwijk, Marten H., Robitaille, Thomas P., Merry, Bruce, Bachetti, Matteo, Günther, H. Moritz, Aldcroft, Thomas L., Alvarado-Montes, Jaime A., Archibald, Anne M., Bódi, Attila, Bapat, Shreyas, Barentsen, Geert, Bazán, Juanjo, Biswas, Manish, Boquien, Médéric, Burke, D. J., Cara, Daria, Cara, Mihai, Conroy, Kyle E, Conseil, Simon, Craig, Matthew W., Cross, Robert M., Cruz, Kelle L., D'Eugenio, Francesco, Dencheva, Nadia, Devillepoix, Hadrien A. R., Dietrich, Jörg P., Eigenbrot, Arthur Davis, Erben, Thomas, Ferreira, Leonardo, Foreman-Mackey, Daniel, Fox, Ryan, Freij, Nabil, Garg, Suyog, Geda, Robel, Glattly, Lauren, Gondhalekar, Yash, Gordon, Karl D., Grant, David, Greenfield, Perry, Groener, Austen M., Guest, Steve, Gurovich, Sebastian, Handberg, Rasmus, Hart, Akeem, Hatfield-Dodds, Zac, Homeier, Derek, Hosseinzadeh, Griffin, Jenness, Tim, Jones, Craig K., Joseph, Prajwel, Kalmbach, J. Bryce, Karamehmetoglu, Emir, Kałuszyński, Mikołaj, Kelley, Michael S. P., Kern, Nicholas, Kerzendorf, Wolfgang E., Koch, Eric W., Kulumani, Shankar, Lee, Antony, Ly, Chun, Ma, Zhiyuan, MacBride, Conor, Maljaars, Jakob M., Muna, Demitri, Murphy, N. A., Norman, Henrik, O'Steen, Richard, Oman, Kyle A., Pacifici, Camilla, Pascual, Sergio, Pascual-Granado, J., Patil, Rohit R., Perren, Gabriel I, Pickering, Timothy E., Rastogi, Tanuj, Roulston, Benjamin R., Ryan, Daniel F, Rykoff, Eli S., Sabater, Jose, Sakurikar, Parikshit, Salgado, Jesús, Sanghi, Aniket, Saunders, Nicholas, Savchenko, Volodymyr, Schwardt, Ludwig, Seifert-Eckert, Michael, Shih, Albert Y., Jain, Anany Shrey, Shukla, Gyanendra, Sick, Jonathan, Simpson, Chris, Singanamalla, Sudheesh, Singer, Leo P., Singhal, Jaladh, Sinha, Manodeep, Sipőcz, Brigitta M., Spitler, Lee R., Stansby, David, Streicher, Ole, Šumak, Jani, Swinbank, John D., Taranu, Dan S., Tewary, Nikita, Tremblay, Grant R., de Val-Borro, Miguel, Van Kooten, Samuel J., Vasović, Zlatan, Verma, Shresth, Cardoso, José Vinícius de Miranda, Williams, Peter K. G., Wilson, Tom J., Winkel, Benjamin, Wood-Vasey, W. M., Xue, Rui, Yoachim, Peter, ZHANG, Chen, Zonca, Andrea
The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package $\texttt{a
Externí odkaz:
http://arxiv.org/abs/2206.14220