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of 2 374
pro vyhledávání: '"Chen, ZhiHong"'
Reinforcement learning tasks in real-world scenarios often involve large, high-dimensional action spaces, leading to challenges such as convergence difficulties, instability, and high computational complexity. It is widely acknowledged that tradition
Externí odkaz:
http://arxiv.org/abs/2412.12605
Autor:
Paschali, Magdalini, Chen, Zhihong, Blankemeier, Louis, Varma, Maya, Youssef, Alaa, Bluethgen, Christian, Langlotz, Curtis, Gatidis, Sergios, Chaudhari, Akshay
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on
Externí odkaz:
http://arxiv.org/abs/2411.18730
Autor:
Prakash, Eva, Valanarasu, Jeya Maria Jose, Chen, Zhihong, Reis, Eduardo Pontes, Johnston, Andrew, Pareek, Anuj, Bluethgen, Christian, Gatidis, Sergios, Olsen, Cameron, Chaudhari, Akshay, Ng, Andrew, Langlotz, Curtis
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials a
Externí odkaz:
http://arxiv.org/abs/2411.18602
Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primari
Externí odkaz:
http://arxiv.org/abs/2411.04097
Autor:
Hein, Dennis, Chen, Zhihong, Ostmeier, Sophie, Xu, Justin, Varma, Maya, Reis, Eduardo Pontes, Michalson, Arne Edward, Bluethgen, Christian, Shin, Hyun Joo, Langlotz, Curtis, Chaudhari, Akshay S
Radiologists play a crucial role by translating medical images into medical reports. However, the field faces staffing shortages and increasing workloads. While automated approaches using vision-language models (VLMs) show promise as assistants, they
Externí odkaz:
http://arxiv.org/abs/2410.07025
Autor:
Xu, Justin, Chen, Zhihong, Johnston, Andrew, Blankemeier, Louis, Varma, Maya, Hom, Jason, Collins, William J., Modi, Ankit, Lloyd, Robert, Hopkins, Benjamin, Langlotz, Curtis, Delbrouck, Jean-Benoit
Publikováno v:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing (2024) 85-98
Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospit
Externí odkaz:
http://arxiv.org/abs/2409.16603
Autor:
Blankemeier, Louis, Cohen, Joseph Paul, Kumar, Ashwin, Van Veen, Dave, Gardezi, Syed Jamal Safdar, Paschali, Magdalini, Chen, Zhihong, Delbrouck, Jean-Benoit, Reis, Eduardo, Truyts, Cesar, Bluethgen, Christian, Jensen, Malte Engmann Kjeldskov, Ostmeier, Sophie, Varma, Maya, Valanarasu, Jeya Maria Jose, Fang, Zhongnan, Huo, Zepeng, Nabulsi, Zaid, Ardila, Diego, Weng, Wei-Hung, Junior, Edson Amaro, Ahuja, Neera, Fries, Jason, Shah, Nigam H., Johnston, Andrew, Boutin, Robert D., Wentland, Andrew, Langlotz, Curtis P., Hom, Jason, Gatidis, Sergios, Chaudhari, Akshay S.
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current radiologist shortage, there is a large impetus to use artificial intelligence to alleviate the
Externí odkaz:
http://arxiv.org/abs/2406.06512
Autor:
Chambon, Pierre, Delbrouck, Jean-Benoit, Sounack, Thomas, Huang, Shih-Cheng, Chen, Zhihong, Varma, Maya, Truong, Steven QH, Chuong, Chu The, Langlotz, Curtis P.
Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to
Externí odkaz:
http://arxiv.org/abs/2405.19538
Autor:
Ostmeier, Sophie, Xu, Justin, Chen, Zhihong, Varma, Maya, Blankemeier, Louis, Bluethgen, Christian, Michalson, Arne Edward, Moseley, Michael, Langlotz, Curtis, Chaudhari, Akshay S, Delbrouck, Jean-Benoit
Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existing automatic evaluation metrics either suffer from failing to consider
Externí odkaz:
http://arxiv.org/abs/2405.03595
Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on extending these
Externí odkaz:
http://arxiv.org/abs/2402.15116