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Glance and Focus Reinforcement for Pan-cancer Screening

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Pan-cancer screeningAI for healthcare
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. We conduct training and validation on a large-scale pan-cancer dataset comprising 5,117 CT scans. 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). In addition, through discarding redundant regions, GF-Screen reduces the computation costs by 5.7 times, significantly improving inference efficiency. The superior performance of GF-Screen remarks a novel and practical breakthrough in pan-cancer screening. Codes will be available.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces GF-Screen, a two-stage reinforcement learning framework that combines a Glance model for region localization and a Focus model for lesion segmentation in pan-cancer CT screening. It resides in the 'Reinforcement Learning and Attention-based Screening Strategies' leaf, which contains only one sibling paper among the 45 total papers in the taxonomy. This sparse population suggests the work occupies a relatively underexplored niche within the broader deep learning models branch, where most research concentrates on foundation models, organ-specific segmentation, or single-cancer detection approaches.

The taxonomy tree reveals that neighboring leaves include 'Foundation Models and Unified Multi-cancer Frameworks' (three papers) and 'Organ and Lesion Segmentation in Abdominal CT' (four papers), both of which address multi-cancer detection but through different architectural paradigms. The scope note for the original paper's leaf explicitly excludes standard supervised learning without RL or attention-based region selection, positioning GF-Screen at the boundary between efficiency-driven sampling strategies and comprehensive whole-volume analysis. The broader 'Deep Learning Models' branch encompasses seven distinct leaves, indicating that reinforcement learning approaches represent one of several competing methodological directions rather than the dominant paradigm.

Among the three contributions analyzed, the literature search examined nine candidate papers total. The core GF-Screen framework examined three candidates with zero refutable matches, the Group Relative Learning paradigm examined five candidates with zero refutable matches, and the extension of RL techniques to pan-cancer screening examined one candidate with zero refutable matches. These statistics suggest that within the limited search scope of top-K semantic matches, no prior work was identified that directly overlaps with the specific combination of glance-focus architecture, group-based reward mechanisms, and pan-cancer application domain. However, the small candidate pool (nine papers) means the analysis captures only a narrow slice of potentially relevant literature.

Based on the limited search scope, the work appears to introduce a novel combination of reinforcement learning and two-stage screening for pan-cancer CT analysis, though the sparse taxonomy leaf and small candidate pool prevent definitive conclusions about field-wide novelty. The analysis covers top-nine semantic matches and does not exhaustively survey all attention mechanisms, region proposal methods, or multi-stage detection frameworks in medical imaging. A more comprehensive search across computer vision and medical AI venues would be needed to fully assess whether similar glance-focus paradigms exist in adjacent domains.

Taxonomy

Core-task Taxonomy Papers
45
3
Claimed Contributions
9
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Pan-cancer screening in large-scale CT scans. The field is organized around several complementary directions that together address the technical, clinical, and safety challenges of detecting multiple cancer types from imaging data. Deep learning models for pan-cancer detection and segmentation form a central branch, encompassing both traditional convolutional architectures and newer reinforcement learning or attention-based strategies that prioritize efficiency when scanning large volumes. Multimodal and hybrid imaging approaches integrate CT with PET, liquid biopsy, or other modalities to improve sensitivity and specificity. Clinical screening programs and feasibility studies evaluate real-world deployment, while radiation risk assessment and epidemiology quantify the trade-offs of repeated imaging exposure. Additional branches cover intraoperative guidance technologies, multi-cancer risk prediction models that combine imaging with clinical or genomic features, and metastasis detection methods that characterize tumor spread across anatomical sites. Within the deep learning branch, a particularly active line of work explores how to balance computational cost against detection performance when processing whole-body or large-field CT volumes. Glance and Focus[0] exemplifies this trend by employing a two-stage reinforcement learning strategy that first identifies regions of interest at low resolution and then applies detailed analysis selectively, contrasting with end-to-end segmentation frameworks like nnU-Net Cross-Cancer[5] or ensemble approaches such as Ensemble Multi-Cancer[1] that process entire scans uniformly. Pan-cancer Screening[41] shares a similar motivation of scalable multi-organ analysis, though it may differ in the specific attention or sampling mechanisms used. These reinforcement learning and attention-based methods sit at the intersection of algorithmic efficiency and clinical feasibility, addressing the open question of how to deploy pan-cancer screening at population scale without prohibitive computational or radiation costs.

Claimed Contributions

GF-Screen: Glance and Focus reinforcement learning framework for pan-cancer screening

The authors propose a two-stage framework that mimics radiologists' diagnostic strategy. A Glance model localizes diseased regions at a coarse level, and a Focus model performs precise lesion segmentation. The Glance model is optimized via reinforcement learning using segmentation results from the Focus model as rewards.

3 retrieved papers
Group Relative Learning paradigm for sub-volume selection

The authors develop a new reinforcement learning optimization method that compares sub-volumes within groups to prioritize those with high segmentation advantages and discard those with low advantages. This approach improves efficiency and reduces false positives without requiring an extra value model.

5 retrieved papers
Extension of reinforcement learning techniques to pan-cancer screening challenges

The authors claim to be the first to apply state-of-the-art reinforcement learning methods to address the unique challenges of pan-cancer screening, such as extreme foreground-background imbalance and the need to focus on diseased regions while discarding redundant healthy regions.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

GF-Screen: Glance and Focus reinforcement learning framework for pan-cancer screening

The authors propose a two-stage framework that mimics radiologists' diagnostic strategy. A Glance model localizes diseased regions at a coarse level, and a Focus model performs precise lesion segmentation. The Glance model is optimized via reinforcement learning using segmentation results from the Focus model as rewards.

Contribution

Group Relative Learning paradigm for sub-volume selection

The authors develop a new reinforcement learning optimization method that compares sub-volumes within groups to prioritize those with high segmentation advantages and discard those with low advantages. This approach improves efficiency and reduces false positives without requiring an extra value model.

Contribution

Extension of reinforcement learning techniques to pan-cancer screening challenges

The authors claim to be the first to apply state-of-the-art reinforcement learning methods to address the unique challenges of pan-cancer screening, such as extreme foreground-background imbalance and the need to focus on diseased regions while discarding redundant healthy regions.

Table of Contents