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pro vyhledávání: '"Chaves, Juan Manuel Zambrano"'
Autor:
Chaves, Juan Manuel Zambrano, Wang, Eric, Tu, Tao, Vaishnav, Eeshit Dhaval, Lee, Byron, Mahdavi, S. Sara, Semturs, Christopher, Fleet, David, Natarajan, Vivek, Azizi, Shekoofeh
Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable. However, the majority of current AI approaches address only a n
Externí odkaz:
http://arxiv.org/abs/2406.06316
Autor:
Chaves, Juan Manuel Zambrano, Huang, Shih-Cheng, Xu, Yanbo, Xu, Hanwen, Usuyama, Naoto, Zhang, Sheng, Wang, Fei, Xie, Yujia, Khademi, Mahmoud, Yang, Ziyi, Awadalla, Hany, Gong, Julia, Hu, Houdong, Yang, Jianwei, Li, Chunyuan, Gao, Jianfeng, Gu, Yu, Wong, Cliff, Wei, Mu, Naumann, Tristan, Chen, Muhao, Lungren, Matthew P., Chaudhari, Akshay, Yeung-Levy, Serena, Langlotz, Curtis P., Wang, Sheng, Poon, Hoifung
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising results on some biomedical benchmarks, there are still major challenges
Externí odkaz:
http://arxiv.org/abs/2403.08002
Autor:
Van Veen, Dave, Van Uden, Cara, Attias, Maayane, Pareek, Anuj, Bluethgen, Christian, Polacin, Malgorzata, Chiu, Wah, Delbrouck, Jean-Benoit, Chaves, Juan Manuel Zambrano, Langlotz, Curtis P., Chaudhari, Akshay S., Pauly, John
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or cli
Externí odkaz:
http://arxiv.org/abs/2305.01146
Autor:
Blankemeier, Louis, Desai, Arjun, Chaves, Juan Manuel Zambrano, Wentland, Andrew, Yao, Sally, Reis, Eduardo, Jensen, Malte, Bahl, Bhanushree, Arora, Khushboo, Patel, Bhavik N., Lenchik, Leon, Willis, Marc, Boutin, Robert D., Chaudhari, Akshay S.
Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to extract quantitative body composition metrics to analyze tissue volume
Externí odkaz:
http://arxiv.org/abs/2302.06568
Autor:
Chambon, Pierre, Bluethgen, Christian, Delbrouck, Jean-Benoit, Van der Sluijs, Rogier, Połacin, Małgorzata, Chaves, Juan Manuel Zambrano, Abraham, Tanishq Mathew, Purohit, Shivanshu, Langlotz, Curtis P., Chaudhari, Akshay
Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in generating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture
Externí odkaz:
http://arxiv.org/abs/2211.12737
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