Zobrazeno 1 - 10
of 966
pro vyhledávání: '"Valle, Javier A"'
Autor:
Sharma, Harshita, Salvatelli, Valentina, Srivastav, Shaury, Bouzid, Kenza, Bannur, Shruthi, Castro, Daniel C., Ilse, Maximilian, Bond-Taylor, Sam, Ranjit, Mercy Prasanna, Falck, Fabian, Pérez-García, Fernando, Schwaighofer, Anton, Richardson, Hannah, Wetscherek, Maria Teodora, Hyland, Stephanie L., Alvarez-Valle, Javier
There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretat
Externí odkaz:
http://arxiv.org/abs/2411.11362
Autor:
Castro, Daniel C., Bustos, Aurelia, Bannur, Shruthi, Hyland, Stephanie L., Bouzid, Kenza, Wetscherek, Maria Teodora, Sánchez-Valverde, Maria Dolores, Jaques-Pérez, Lara, Pérez-Rodríguez, Lourdes, Takeda, Kenji, Salinas, José María, Alvarez-Valle, Javier, Herrero, Joaquín Galant, Pertusa, Antonio
Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no man
Externí odkaz:
http://arxiv.org/abs/2411.05085
Autor:
Codella, Noel C. F., Jin, Ying, Jain, Shrey, Gu, Yu, Lee, Ho Hin, Abacha, Asma Ben, Santamaria-Pang, Alberto, Guyman, Will, Sangani, Naiteek, Zhang, Sheng, Poon, Hoifung, Hyland, Stephanie, Bannur, Shruthi, Alvarez-Valle, Javier, Li, Xue, Garrett, John, McMillan, Alan, Rajguru, Gaurav, Maddi, Madhu, Vijayrania, Nilesh, Bhimai, Rehaan, Mecklenburg, Nick, Jain, Rupal, Holstein, Daniel, Gaur, Naveen, Aski, Vijay, Hwang, Jenq-Neng, Lin, Thomas, Tarapov, Ivan, Lungren, Matthew, Wei, Mu
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT,
Externí odkaz:
http://arxiv.org/abs/2410.06542
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:
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, 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
Publikováno v:
European Conference on Computer Vision (ECCV) 2024
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:
Pofelski, Alexandre, Valencia, Sergio, Kalcheim, Yoav, Salev, Pavel, Rivera, Alberto, Huang, Chubin, Mawass, Mohamad A., Kronast, Florian, Schuller, Ivan K., Zhu, Yimei, del Valle, Javier
Bulk V2O3 features concomitant metal-insulator (MIT) and structural (SPT) phase transitions at TC ~ 160 K. In thin films, where the substrate clamping can impose geometrical restrictions on the SPT, the epitaxial relation between the V2O3 film and su
Externí odkaz:
http://arxiv.org/abs/2312.09051
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