Glance and Focus Reinforcement for Pan-cancer Screening

2026-01-27

AI Summary

This work addresses the challenges of detecting small and diverse tumor lesions in large-scale CT imaging, where extreme foreground-background imbalance and high false-positive rates hinder performance. To this end, we propose GF-Screen, a novel framework that, for the first time, effectively applies reinforcement learning to pan-cancer screening by emulating radiologists’ “glance-and-focus” strategy: a Glance model coarsely identifies suspicious subregions, while a Focus model performs precise lesion segmentation, with the segmentation outcome serving as a reward signal to guide the Glance model’s region selection. We further introduce a group-wise relative learning paradigm that prioritizes high-advantage predictions within each group to enhance efficiency and suppress false positives. Evaluated on 16 internal and 7 external datasets covering nine lesion types, our method achieves top rank on the MICCAI FLARE25 Pan-Cancer Challenge leaderboard, surpassing the FLARE24 champion by 25.6% in DSC and 28.2% in NSD.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme foreground-background imbalance significantly hinders models from focusing on diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false positives. Inspired by radiologists'glance and focus diagnostic strategy, we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cancer screening. GF-Screen employs a Glance model to localize the diseased regions and a Focus model to precisely segment the lesions, where segmentation results of the Focus model are leveraged to reward the Glance model via Reinforcement Learning (RL). Specifically, the Glance model crops a group of sub-volumes from the entire CT volume and learns to select the sub-volumes with lesions for the Focus model to segment. Given that the selecting operation is non-differentiable for segmentation training, we propose to employ the segmentation results to reward the Glance model. To optimize the Glance model, we introduce a novel group relative learning paradigm, which employs group relative comparison to prioritize high-advantage predictions and discard low-advantage predictions within sub-volume groups, not only improving efficiency but also reducing false positives. In this way, for the first time, we effectively extend cutting-edge RL techniques to tackle the specific challenges in pan-cancer screening. Extensive experiments on 16 internal and 7 external datasets across 9 lesion types demonstrated the effectiveness of GF-Screen. Notably, GF-Screen leads the public validation leaderboard of MICCAI FLARE25 pan-cancer challenge, surpassing the FLARE24 champion solution by a large margin (+25.6% DSC and +28.2% NSD).

Problem

• pan-cancer screening
• tiny lesion localization
• foreground-background imbalance
• false positives
• large-scale CT scans

Innovation

• Glance and Focus
• Reinforcement Learning
• Pan-cancer Screening
• Group Relative Learning
• Lesion Localization