Chest X Ray
Chest X-ray (CXR) analysis is a crucial diagnostic tool in healthcare, with research focusing on improving accuracy, efficiency, and accessibility of interpretation. Current efforts center on developing and refining deep learning models, including convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating techniques like transfer learning, self-supervised learning, and multi-modal approaches that integrate textual reports and other patient data. These advancements aim to automate report generation, improve disease detection (including in under-resourced settings), and enhance the overall quality and speed of radiological diagnosis, ultimately impacting patient care and clinical workflow.
Papers
Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure
Walt Williams, Rohan Doshi, Yanran Li, Kexuan Liang
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
Syed Muhammad Anwar, Abhijeet Parida, Sara Atito, Muhammad Awais, Gustavo Nino, Josef Kitler, Marius George Linguraru
Can we Adopt Self-supervised Pretraining for Chest X-Rays?
Arsh Verma, Makarand Tapaswi
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari