泛癌筛查中的一瞥聚焦强化机制

Glance and Focus Reinforcement for Pan-cancer Screening

January 27, 2026

作者: Linshan Wu, Jiaxin Zhuang, Hao Chen

cs.AI

摘要

大規模CT掃描中的泛癌篩查對現有AI方法仍具挑戰性，主要難點在於定位大尺寸CT體積數據中多類微小病灶的困難性。極端的病灶-背景比例失衡嚴重阻礙模型聚焦病變區域，而對健康區域的冗餘關注不僅降低效率，更會增加假陽性結果。受放射科醫師"掃視-聚焦"診斷策略啟發，我們提出GF-Screen框架——一種用於泛癌篩查的強化學習掃視-聚焦機制。該框架採用掃視模型定位病變區域，聚焦模型精準分割病灶，並通過強化學習將聚焦模型的分割結果作為獎勵信號反饋給掃視模型。具體而言，掃視模型從全尺寸CT體積中截取多個子區域，學習篩選含病灶子區域供聚焦模型分割。針對選擇操作不可微分導致的訓練難題，我們創新性地利用分割結果構建獎勵機制。為優化掃視模型，提出群組相對學習範式，通過組內優勢度比較優先保留高價值預測、淘汰低價值預測，在提升效率的同時有效抑制假陽性。此方法首次成功將前沿強化學習技術應用於泛癌篩查的特殊挑戰。在涵蓋9類病灶的16個內部數據集和7個外部數據集上的大量實驗驗證了GF-Screen的有效性。值得注意的是，該方法在MICCAI FLARE25泛癌挑戰賽公開驗證榜單中遙遙領先，較FLARE24冠軍方案實現顯著提升（DSC提升25.6%，NSD提升28.2%）。

English

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).