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
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
Felix J. Dorfner, Liv Jürgensen, Leonhard Donle, Fares Al Mohamad, Tobias R. Bodenmann, Mason C. Cleveland, Felix Busch, Lisa C. Adams, James Sato, Thomas Schultz, Albert E. Kim, Jameson Merkow, Keno K. Bressem, Christopher P. Bridge
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier
COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics
Yifan Wu, Hayden Gunraj, Chi-en Amy Tai, Alexander Wong
Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
Haoran Lai, Qingsong Yao, Zhiyang He, Xiaodong Tao, S Kevin Zhou