Zobrazeno 1 - 10
of 601
pro vyhledávání: '"Sanchez, Clara"'
Autor:
Patil, Akshay, García-Sánchez, Clara
In this study, we introduced a simple yet innovative method to trigger turbulence in a channel flow to achieve statistically stationary flow conditions. We compare this new method based on synthetically generated three-dimensional turbulence with two
Externí odkaz:
http://arxiv.org/abs/2411.11416
The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified using prompts, such as bounding boxes or points. With these FMs becoming p
Externí odkaz:
http://arxiv.org/abs/2411.08629
Autor:
de Vente, Coen, Islam, Mohammad Mohaiminul, Valmaggia, Philippe, Hoyng, Carel, Tufail, Adnan, Sánchez, Clara I.
High anisotropy in volumetric medical images can lead to the inconsistent quantification of anatomical and pathological structures. Particularly in optical coherence tomography (OCT), slice spacing can substantially vary across and within datasets, s
Externí odkaz:
http://arxiv.org/abs/2410.09862
Autor:
Sogancioglu, Ecem, van Ginneken, Bram, Behrendt, Finn, Bengs, Marcel, Schlaefer, Alexander, Radu, Miron, Xu, Di, Sheng, Ke, Scalzo, Fabien, Marcus, Eric, Papa, Samuele, Teuwen, Jonas, Scholten, Ernst Th., Schalekamp, Steven, Hendrix, Nils, Jacobs, Colin, Hendrix, Ward, Sánchez, Clara I, Murphy, Keelin
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lu
Externí odkaz:
http://arxiv.org/abs/2401.02192
Autor:
Sandoval, Brandon, Foord, Adi, Allen, Steven W., Volonteri, Marta, Stemo, Aaron, Chen, Nianyi, Di Matteo, Tiziana, Gultekin, Kayhan, Habouzit, Melanie, Puerto-Sanchez, Clara, Hodges-Kluck, Edmund, Dubois, Yohan
We present an analysis searching for dual AGN among 62 high-redshift ($2.5 < z < 3.5$) X-ray sources selected from publicly available deep Chandra fields. We aim to quantify the frequency of dual AGN in the high-redshift Universe, which holds implica
Externí odkaz:
http://arxiv.org/abs/2312.02311
Purpose: MRI represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled k-space data under motion. In the absence of fully-sampled acquisitions, serving as ground truths,
Externí odkaz:
http://arxiv.org/abs/2311.15856
Autor:
de Vente, Coen, van Ginneken, Bram, Hoyng, Carel B., Klaver, Caroline C. W., Sánchez, Clara I.
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to data from different vendors, their performance tends to drop substantial
Externí odkaz:
http://arxiv.org/abs/2302.03116
Autor:
Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., Maier-Hein, Lena
Publikováno v:
Nature methods, 1-13 (2024)
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in im
Externí odkaz:
http://arxiv.org/abs/2302.01790
Autor:
de Vente, Coen, Vermeer, Koenraad A., Jaccard, Nicolas, Wang, He, Sun, Hongyi, Khader, Firas, Truhn, Daniel, Aimyshev, Temirgali, Zhanibekuly, Yerkebulan, Le, Tien-Dung, Galdran, Adrian, Ballester, Miguel Ángel González, Carneiro, Gustavo, G, Devika R, S, Hrishikesh P, Puthussery, Densen, Liu, Hong, Yang, Zekang, Kondo, Satoshi, Kasai, Satoshi, Wang, Edward, Durvasula, Ashritha, Heras, Jónathan, Zapata, Miguel Ángel, Araújo, Teresa, Aresta, Guilherme, Bogunović, Hrvoje, Arikan, Mustafa, Lee, Yeong Chan, Cho, Hyun Bin, Choi, Yoon Ho, Qayyum, Abdul, Razzak, Imran, van Ginneken, Bram, Lemij, Hans G., Sánchez, Clara I.
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models f
Externí odkaz:
http://arxiv.org/abs/2302.01738
Acquiring fully-sampled MRI $k$-space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, th
Externí odkaz:
http://arxiv.org/abs/2301.08365