Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment

2025-07-09

AI Summary

This work addresses the weakly supervised learning challenge of dense lesion localization in laparoscopic videos of advanced ovarian cancer, where only sparse keypoint annotations—not costly pixel-level masks—are available. We propose the “Crag and Tail” loss function, which reformulates sparse point supervision as a structured heatmap regression task: the Crag term enhances positive-sample responses at ground-truth locations, while the Tail term suppresses spurious activations in unlabeled regions, effectively mitigating localization bias induced by annotation sparsity. Integrated within a keypoint detection framework and augmented by heatmap distillation, our method achieves robust generalization from minimal per-frame pixel-level supervision (e.g., 1–3 keypoints) to high-precision dense lesion localization. Evaluated on a clinical dataset, it attains a Dice coefficient of 92.3%. Ablation studies confirm the efficacy, robustness, and clinical applicability of the proposed approach.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Learning dense carcinosis localization from sparse point labels
• Overcoming annotation scarcity in medical image analysis
• Proposing loss function for efficient sparse label learning

Innovation

• Sparse heatmap regression for dense localization
• Crag and Tail loss minimizes false negatives
• Efficient learning from sparse point annotations