Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Nori, Aditya"'
Autor:
González, Javier, Nori, Aditya V.
Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking. However, there is an ongoing debate about the extent to which LLMs are capable of
Externí odkaz:
http://arxiv.org/abs/2408.08210
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:
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, 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
Synthetic control (SC) methods have gained rapid popularity in economics recently, where they have been applied in the context of inferring the effects of treatments on standard continuous outcomes assuming linear input-output relations. In medical a
Externí odkaz:
http://arxiv.org/abs/2312.00501
Autor:
González, Javier, Nori, Aditya V.
Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information. However, the field lacks a mathematical framework to systematically describe, compare and improve LLMs. We propos
Externí odkaz:
http://arxiv.org/abs/2311.03033
Autor:
González, Javier, Wong, Cliff, Gero, Zelalem, Bagga, Jass, Ueno, Risa, Chien, Isabel, Oravkin, Eduard, Kiciman, Emre, Nori, Aditya, Weerasinghe, Roshanthi, Leidner, Rom S., Piening, Brian, Naumann, Tristan, Bifulco, Carlo, Poon, Hoifung
The rapid digitization of real-world data offers an unprecedented opportunity for optimizing healthcare delivery and accelerating biomedical discovery. In practice, however, such data is most abundantly available in unstructured forms, such as clinic
Externí odkaz:
http://arxiv.org/abs/2311.01301
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
Autor:
Liu, Fangyu, Liu, Qianchu, Bannur, Shruthi, Pérez-García, Fernando, Usuyama, Naoto, Zhang, Sheng, Naumann, Tristan, Nori, Aditya, Poon, Hoifung, Alvarez-Valle, Javier, Oktay, Ozan, Hyland, Stephanie L.
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain l
Externí odkaz:
http://arxiv.org/abs/2303.13386
Autor:
Bannur, Shruthi, Hyland, Stephanie, Liu, Qianchu, Pérez-García, Fernando, Ilse, Maximilian, Castro, Daniel C., Boecking, Benedikt, Sharma, Harshita, Bouzid, Kenza, Thieme, Anja, Schwaighofer, Anton, Wetscherek, Maria, Lungren, Matthew P., Nori, Aditya, Alvarez-Valle, Javier, Oktay, Ozan
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes common
Externí odkaz:
http://arxiv.org/abs/2301.04558