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
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng
COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics
Maya Pavlova, Tia Tuinstra, Hossein Aboutalebi, Andy Zhao, Hayden Gunraj, Alexander Wong