Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ranjit, Mercy"'
Autor:
Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Schwaighofer, Anton, Thieme, Anja, Bond-Taylor, Sam, Ilse, Maximilian, Pérez-García, Fernando, Salvatelli, Valentina, Sharma, Harshita, Meissen, Felix, Ranjit, Mercy, Srivastav, Shaury, Gong, Julia, Codella, Noel C. F., Falck, Fabian, Oktay, Ozan, Lungren, Matthew P., Wetscherek, Maria Teodora, Alvarez-Valle, Javier, Hyland, Stephanie L.
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high
Externí odkaz:
http://arxiv.org/abs/2406.04449
Autor:
Kumar, Somnath, Balloli, Vaibhav, Ranjit, Mercy, Ahuja, Kabir, Ganu, Tanuja, Sitaram, Sunayana, Bali, Kalika, Nambi, Akshay
Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of
Externí odkaz:
http://arxiv.org/abs/2405.18359
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
Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, particularly concerning radiology text, is less explored. In this study
Externí odkaz:
http://arxiv.org/abs/2403.09725
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:
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:
Nambi, Akshay, Balloli, Vaibhav, Ranjit, Mercy, Ganu, Tanuja, Ahuja, Kabir, Sitaram, Sunayana, Bali, Kalika
Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of
Externí odkaz:
http://arxiv.org/abs/2305.17740
We propose Retrieval Augmented Generation (RAG) as an approach for automated radiology report writing that leverages multimodally aligned embeddings from a contrastively pretrained vision language model for retrieval of relevant candidate radiology t
Externí odkaz:
http://arxiv.org/abs/2305.03660
Publikováno v:
ICTACT Journal on Image & Video Processing; May2023, Vol. 13 Issue 4, p3028-3034, 7p