Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Anwar, Syed Muhammad"'
Autor:
Capellán-Martín, Daniel, Jiang, Zhifan, Parida, Abhijeet, Liu, Xinyang, Lam, Van, Nisar, Hareem, Tapp, Austin, Elsharkawi, Sarah, Ledesma-Carbayo, Maria J., Anwar, Syed Muhammad, Linguraru, Marius George
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making proce
Externí odkaz:
http://arxiv.org/abs/2409.08232
Autor:
Shi, Shanghao, Haque, Md Shahedul, Parida, Abhijeet, Linguraru, Marius George, Hou, Y. Thomas, Anwar, Syed Muhammad, Lou, Wenjing
Federated learning (FL) allows a set of clients to collaboratively train a machine-learning model without exposing local training samples. In this context, it is considered to be privacy-preserving and hence has been adopted by medical centers to tra
Externí odkaz:
http://arxiv.org/abs/2407.09972
Autor:
Shaukat, Furqan, Anwar, Syed Muhammad, Parida, Abhijeet, Lam, Van Khanh, Linguraru, Marius George, Shah, Mubarak
Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. Large Visual Language models (VLMs) have been found effectiv
Externí odkaz:
http://arxiv.org/abs/2407.02625
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions
Autor:
Nisar, Hareem, Anwar, Syed Muhammad, Jiang, Zhifan, Parida, Abhijeet, Sanchez-Jacob, Ramon, Nath, Vishwesh, Roth, Holger R., Linguraru, Marius George
Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assistant for biomedicine, can perform multi-modal biomedical image and dat
Externí odkaz:
http://arxiv.org/abs/2407.02604
Autor:
LaBella, Dominic, Schumacher, Katherine, Mix, Michael, Leu, Kevin, McBurney-Lin, Shan, Nedelec, Pierre, Villanueva-Meyer, Javier, Shapey, Jonathan, Vercauteren, Tom, Chia, Kazumi, Al-Salihi, Omar, Leu, Justin, Halasz, Lia, Velichko, Yury, Wang, Chunhao, Kirkpatrick, John, Floyd, Scott, Reitman, Zachary J., Mullikin, Trey, Bagci, Ulas, Sachdev, Sean, Hattangadi-Gluth, Jona A., Seibert, Tyler, Farid, Nikdokht, Puett, Connor, Pease, Matthew W., Shiue, Kevin, Anwar, Syed Muhammad, Faghani, Shahriar, Haider, Muhammad Ammar, Warman, Pranav, Albrecht, Jake, Jakab, András, Moassefi, Mana, Chung, Verena, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Huang, Christina, Coley, Aaron, Ghanta, Siddharth, Schneider, Alex, Sharp, Conrad, Saluja, Rachit, Kofler, Florian, Lohmann, Philipp, Vollmuth, Phillipp, Gagnon, Louis, Adewole, Maruf, Li, Hongwei Bran, Kazerooni, Anahita Fathi, Tahon, Nourel Hoda, Anazodo, Udunna, Moawad, Ahmed W., Menze, Bjoern, Linguraru, Marius George, Aboian, Mariam, Wiestler, Benedikt, Baid, Ujjwal, Conte, Gian-Marco, Rauschecker, Andreas M., Nada, Ayman, Abayazeed, Aly H., Huang, Raymond, de Verdier, Maria Correia, Rudie, Jeffrey D., Bakas, Spyridon, Calabrese, Evan
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target
Externí odkaz:
http://arxiv.org/abs/2405.18383
Autor:
LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, Calabrese, Evan
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axia
Externí odkaz:
http://arxiv.org/abs/2405.09787
Autor:
Parida, Abhijeet, Capellan-Martin, Daniel, Atito, Sara, Awais, Muhammad, Ledesma-Carbayo, Maria J., Linguraru, Marius G., Anwar, Syed Muhammad
Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology re
Externí odkaz:
http://arxiv.org/abs/2402.15534
This paper presents an innovative approach to recognizing personality traits using deep learning (DL) methods applied to electrocardiogram (ECG) signals. Within the framework of detecting the big five personality traits model encompassing extra-versi
Externí odkaz:
http://arxiv.org/abs/2402.04326
Emotion recognition (ER) technology is an integral part for developing innovative applications such as drowsiness detection and health monitoring that plays a pivotal role in contemporary society. This study delves into ER using electroencephalograph
Externí odkaz:
http://arxiv.org/abs/2402.04142
Autor:
Parida, Abhijeet, Jiang, Zhifan, Anwar, Syed Muhammad, Foreman, Nicholas, Stence, Nicholas, Fisher, Michael J., Packer, Roger J., Avery, Robert A., Linguraru, Marius George
For machine learning-based prognosis and diagnosis of rare diseases, such as pediatric brain tumors, it is necessary to gather medical imaging data from multiple clinical sites that may use different devices and protocols. Deep learning-driven harmon
Externí odkaz:
http://arxiv.org/abs/2308.11047