Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI

Autor: Mathys, Melanie, Willi, Marco, Meier, Raphael

Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used m

Externí odkaz: http://arxiv.org/abs/2408.06398

Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?

Autor: Mathys, Melanie, Willi, Marco, Graber, Michael, Meier, Raphael

The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep learning m

Externí odkaz: http://arxiv.org/abs/2403.12207

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Autor: Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, Bakas, Spyridon

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However,

Externí odkaz: http://arxiv.org/abs/2204.10836

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Autor: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal

Publikováno v: Machine.Learning.for.Biomedical.Imaging. 1 (2022)

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e

Externí odkaz: http://arxiv.org/abs/2112.10074

Combining unsupervised and supervised learning for predicting the final stroke lesion

Autor: Pinto, Adriano, Pereira, Sérgio, Meier, Raphael, Wiest, Roland, Alves, Victor, Reyes, Mauricio, Silva, Carlos A.

Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selec

Externí odkaz: http://arxiv.org/abs/2101.00489