Zobrazeno 1 - 10
of 443
pro vyhledávání: '"Lungren, Matthew P"'
Autor:
Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Schwaighofer, Anton, Bond-Taylor, Sam, Ilse, Maximilian, Pérez-García, Fernando, Salvatelli, Valentina, Sharma, Harshita, Meissen, Felix, Ranjit, Mercy, Srivastav, Shaury, Gong, Julia, Falck, Fabian, Oktay, Ozan, Thieme, Anja, Lungren, Matthew P., Wetscherek, Maria Teodora, Alvarez-Valle, Javier, Hyland, Stephanie L.
Radiology reporting is a complex task that requires detailed image understanding, integration of multiple inputs, including comparison with prior imaging, and precise language generation. This makes it ideal for the development and use of generative
Externí odkaz:
http://arxiv.org/abs/2406.04449
Autor:
Thieme, Anja, Rajamohan, Abhijith, Cooper, Benjamin, Groombridge, Heather, Simister, Robert, Wong, Barney, Woznitza, Nicholas, Pinnock, Mark Ames, Wetscherek, Maria Teodora, Morrison, Cecily, Richardson, Hannah, Pérez-García, Fernando, Hyland, Stephanie L., Bannur, Shruthi, Castro, Daniel C., Bouzid, Kenza, Schwaighofer, Anton, Ranjit, Mercy, Sharma, Harshita, Lungren, Matthew P., Oktay, Ozan, Alvarez-Valle, Javier, Nori, Aditya, Harris, Stephen, Jacob, Joseph
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the fe
Externí odkaz:
http://arxiv.org/abs/2405.05299
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:
Yildirim, Nur, Richardson, Hannah, Wetscherek, Maria T., Bajwa, Junaid, Jacob, Joseph, Pinnock, Mark A., Harris, Stephen, de Castro, Daniel Coelho, Bannur, Shruthi, Hyland, Stephanie L., Ghosh, Pratik, Ranjit, Mercy, Bouzid, Kenza, Schwaighofer, Anton, Pérez-García, Fernando, Sharma, Harshita, Oktay, Ozan, Lungren, Matthew, Alvarez-Valle, Javier, Nori, Aditya, Thieme, Anja
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language mode
Externí odkaz:
http://arxiv.org/abs/2402.14252
Autor:
Pérez-García, Fernando, Sharma, Harshita, Bond-Taylor, Sam, Bouzid, Kenza, Salvatelli, Valentina, Ilse, Maximilian, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Lungren, Matthew P., Wetscherek, Maria, Codella, Noel, Hyland, Stephanie L., Alvarez-Valle, Javier, Oktay, Ozan
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains. However,
Externí odkaz:
http://arxiv.org/abs/2401.10815
Autor:
Pérez-García, Fernando, Bond-Taylor, Sam, Sanchez, Pedro P., van Breugel, Boris, Castro, Daniel C., Sharma, Harshita, Salvatelli, Valentina, Wetscherek, Maria T. A., Richardson, Hannah, Lungren, Matthew P., Nori, Aditya, Alvarez-Valle, Javier, Oktay, Ozan, Ilse, Maximilian
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shi
Externí odkaz:
http://arxiv.org/abs/2312.12865
Autor:
Abacha, Asma Ben, Santamaria-Pang, Alberto, Lee, Ho Hin, Merkow, Jameson, Cai, Qin, Devarakonda, Surya Teja, Islam, Abdullah, Gong, Julia, Lungren, Matthew P., Lin, Thomas, Codella, Noel C, Tarapov, Ivan
The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged. 3D image retriev
Externí odkaz:
http://arxiv.org/abs/2311.13752
Autor:
Hyland, Stephanie L., Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Ranjit, Mercy, Schwaighofer, Anton, Pérez-García, Fernando, Salvatelli, Valentina, Srivastav, Shaury, Thieme, Anja, Codella, Noel, Lungren, Matthew P., Wetscherek, Maria Teodora, Oktay, Ozan, Alvarez-Valle, Javier
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with p
Externí odkaz:
http://arxiv.org/abs/2311.13668
Autor:
Huang, Shih-Cheng, Huo, Zepeng, Steinberg, Ethan, Chiang, Chia-Chun, Lungren, Matthew P., Langlotz, Curtis P., Yeung, Serena, Shah, Nigam H., Fries, Jason A.
Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimod
Externí odkaz:
http://arxiv.org/abs/2311.10798
Autor:
Liu, Qianchu, Hyland, Stephanie, Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Wetscherek, Maria Teodora, Tinn, Robert, Sharma, Harshita, Pérez-García, Fernando, Schwaighofer, Anton, Rajpurkar, Pranav, Khanna, Sameer Tajdin, Poon, Hoifung, Usuyama, Naoto, Thieme, Anja, Nori, Aditya V., Lungren, Matthew P., Oktay, Ozan, Alvarez-Valle, Javier
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performa
Externí odkaz:
http://arxiv.org/abs/2310.14573