Zobrazeno 1 - 10
of 468
pro vyhledávání: '"Wetscherek A"'
Accurate and reliable Magnetic Resonance Imaging (MRI) analysis is particularly important for adaptive radiotherapy, a recent medical advance capable of improving cancer diagnosis and treatment. Recent studies have shown that IVIM-NET, a deep neural
Externí odkaz:
http://arxiv.org/abs/2407.05521
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
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:
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:
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:
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
Autor:
Boecking, Benedikt, Usuyama, Naoto, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Hyland, Stephanie, Wetscherek, Maria, Naumann, Tristan, Nori, Aditya, Alvarez-Valle, Javier, Poon, Hoifung, Oktay, Ozan
Publikováno v:
Computer Vision - ECCV 2022, LNCS vol 13696, pp 1-21
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional ch
Externí odkaz:
http://arxiv.org/abs/2204.09817