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pro vyhledávání: '"Srivastav, Shaury"'
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
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:
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
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