Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Learning from sparse labels is a challenge commonplace in the medical domain.
This is due to numerous factors, such as annotation cost, and is especially
true for newly introduced tasks. When dense pixel-level annotations are needed,
this becomes even more unfeasible. However, being able to learn from just a few
annotations at the pixel-level, while extremely difficult and underutilized,
can drive progress in studies where perfect annotations are not immediately
available. This work tackles the challenge of learning the dense prediction
task of keypoint localization from a few point annotations in the context of 2d
carcinosis keypoint localization from laparoscopic video frames for diagnostic
planning of advanced ovarian cancer patients. To enable this, we formulate the
problem as a sparse heatmap regression from a few point annotations per image
and propose a new loss function, called Crag and Tail loss, for efficient
learning. Our proposed loss function effectively leverages positive sparse
labels while minimizing the impact of false negatives or missed annotations.
Through an extensive ablation study, we demonstrate the effectiveness of our
approach in achieving accurate dense localization of carcinosis keypoints,
highlighting its potential to advance research in scenarios where dense
annotations are challenging to obtain.
