LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

Understand

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response.

• Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets.
• However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans.
• Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution.