Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Aerts, Hugo JWL."'
Autor:
Hagopian, Raffi, Strebel, Timothy, Bernatz, Simon, Myers, Gregory A, Offerman, Erik, Zuniga, Eric, Kim, Cy Y, Ng, Angie T, Iwaz, James A, Singh, Sunny P, Carey, Evan P, Kim, Michael J, Schaefer, R Spencer, Yu, Jeannie, Gentili, Amilcare, Aerts, Hugo JWL
Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algor
Externí odkaz:
http://arxiv.org/abs/2409.09968
Autor:
Häntze, Hartmut, Xu, Lina, Mertens, Christian J., Dorfner, Felix J., Donle, Leonhard, Busch, Felix, Kader, Avan, Ziegelmayer, Sebastian, Bayerl, Nadine, Navab, Nassir, Rueckert, Daniel, Schnabel, Julia, Aerts, Hugo JWL, Truhn, Daniel, Bamberg, Fabian, Weiß, Jakob, Schlett, Christopher L., Ringhof, Steffen, Niendorf, Thoralf, Pischon, Tobias, Kauczor, Hans-Ulrich, Nonnenmacher, Tobias, Kröncke, Thomas, Völzke, Henry, Schulz-Menger, Jeanette, Maier-Hein, Klaus, Prokop, Mathias, van Ginneken, Bram, Hering, Alessa, Makowski, Marcus R., Adams, Lisa C., Bressem, Keno K.
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT sca
Externí odkaz:
http://arxiv.org/abs/2405.06463
Autor:
Adams, Lisa, Busch, Felix, Han, Tianyu, Excoffier, Jean-Baptiste, Ortala, Matthieu, Löser, Alexander, Aerts, Hugo JWL., Kather, Jakob Nikolas, Truhn, Daniel, Bressem, Keno
Background: Recent advancements in large language models (LLMs) offer potential benefits in healthcare, particularly in processing extensive patient records. However, existing benchmarks do not fully assess LLMs' capability in handling real-world, le
Externí odkaz:
http://arxiv.org/abs/2401.14490
Autor:
Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, Starmans, Martijn P A
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinica
Externí odkaz:
http://arxiv.org/abs/2309.12325
Autor:
Guevara, Marco, Chen, Shan, Thomas, Spencer, Chaunzwa, Tafadzwa L., Franco, Idalid, Kann, Benjamin, Moningi, Shalini, Qian, Jack, Goldstein, Madeleine, Harper, Susan, Aerts, Hugo JWL, Savova, Guergana K., Mak, Raymond H., Bitterman, Danielle S.
Publikováno v:
NPJ Digit Med. 2024 Jan 11;7(1):6
Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EH
Externí odkaz:
http://arxiv.org/abs/2308.06354
Autor:
Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, Fedorov, Andrey
Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-ba
Externí odkaz:
http://arxiv.org/abs/2306.00150
Autor:
Chen, Shan, Li, Yingya, Lu, Sheng, Van, Hoang, Aerts, Hugo JWL, Savova, Guergana K., Bitterman, Danielle S.
Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such
Externí odkaz:
http://arxiv.org/abs/2304.02496
Autor:
Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo JWL, Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., Bitterman, Danielle S.
Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed nat
Externí odkaz:
http://arxiv.org/abs/2303.13722
Autor:
Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo JWL., Löser, Alexander
Publikováno v:
Expert Systems with Applications 2024;237(21):121598
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2303.08179
Autor:
Adams, Lisa C., Busch, Felix, Truhn, Daniel, Makowski, Marcus R., Aerts, Hugo JWL., Bressem, Keno K.
Publikováno v:
J Med Internet Res 2023;25:e43110
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge. Her
Externí odkaz:
http://arxiv.org/abs/2209.13696