Glance and Focus Reinforcement for Pan-cancer Screening
Authors: Linshan Wu
Jia-Xin Zhuang
Hao Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Pan-cancer screening
AI for healthcare
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Inspired by radiologists' diagnostic strategy, we propose a Glance-and-Focus reinforcement learning framework for pan-cancer screening: localize tiny lesions in large CT and discard healthy regions, improving efficiency and reducing false positives.

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. Code is available at https://github.com/Luffy03/GF-Screen.

Published as a conference paper at ICLR 2026
GLANCE AND FOCUS REMEET FOR PAN- CANCER SCREENING
Linshan Wu, Jiaxin Zhuang & Hao Chen'
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Hong Kong, China {1 (linshan. wu, jzhuangad) econnect. ust. hk, jhcecse. ust.
ABSTRACT
Pan-cancer screening large- CT scans remains challenging for existing Al methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme
imbalance signifi- cantly hinders models from focusing diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false posi- tives. Inspired by radiologists' glance and focus diagnostic strategy. we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cances screening. GF-Screen employs 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 group of sub-volumer from the entire CT volume and learns to select the sub-volumes with lesions for the Fo- cus model to segment. Given that the selecting operation is con-differentiable for segmentation training, we propose to employ the segmentation results reward the Glance model. To optimize the Glance model, we introduce novel group relative learning paradigm, which employs group relative comparison to priori- tize high-advantage predictions and discard low-advantage predictions within sub- volume groups, not only improving efficiency but also reducing false positives. this way, for the first time, we effectively extend cutting-edge RL techniques tackle the specific challenges in pan-cancer screening. We conduct training and validation a large-scale pan-cancer dataset comprising 5,117 CT scans. Ex- tensive experiments on 16 internal and 7 external datasets across 9 lesion types demonstrated the effectiveness of GF-Screen. Notably, GF-SSreen leads the pub- lic validation leaderboard of MICCAI FLARE25 pan-cancer challenge, surpass- ing the FLARE24 champion solution by 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 novel and practical breakthrough in pan-cancer screening. Code available httpps://itthb..cmm. com//affly0///FFSSeee
INTRODUCTION
Cancer is leading cause of death worldwide (Bray et al., 2024), and effective cancer screening is crucial to reducing the mortality rate of patients (Shich et al., 2016 McKinney et al. 2020: Jiang
et al. 2022). Al-driven cancer screening in large-scale Computed Tomography (CT) scans has
ceived increasing attention in clinical applications (Tsensee et al. 2021 Cao et al. 2023 Hu et al.)
2025), since CT is low-cost and commonly-used imaging protocol in routine physical examina- tion Pickkaart et al. 2020 2023). Specifically, pan-cancer screening aims to develop one universal model detect and segment different types of lesions in large-scale CT scans (Ma et al. 2024 Chen
et al. 2023 Jiang et al. 2024 2025), which has profound significance in clinical practice.
"Corresponding Author.

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Published as a conference paper at ICLR 2026
00 Pan-canee-dsttatt
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(a) The pan-cancer dataset used in this study, encompassing 5,117 CT scans across 9 differ- ent types of lesions from 16 internal and 7 external datasets. (b) Significant prreground-backgrouug imbalance: in our dataset, Iesions occupy only 0.085% area proportions in CT volumes. (c) Compar- isons in pan-cancer segmentation. (d) Comparisons in pan-cancer detection. (c) Inference efficiency (GFLOP per scan) and segmentation DSC on the FLARRE23 validation dataset. Compared with the second-best model, GF-Sereen is 5.7x faster with higher segmentation DSC. (f) Comparisons on the FLARE25 challenge validation leaderboard. GF-Screen outperforms the second-ranked algorithm (champion solution of FLARE24) by large margin (+25.6% DSC and +28.2% NSD).
Figure I:
Although some promising results have been demonstrated in recent cancer screening works Yan jet al. 2018 Cao al., 2023 Hu et al. 2025 Zhang et al., 2025a), it remains challenging for the development of pan-cancer screening models (Ma et al. 2024). Primarily, lesions typically imbalance poses significant obstacle for models to localize diverse lesion types across different organs. In addition, the redundant focus on healthy regions not only leads to higher false positives but also impedes the screening efficiency. presenting a critical barrier to real-world deployment of AI models (Ma et al.) 2024]. To address these challenges, we highlight that effective pan-cancer screening models should be capable of focusing on diseased regions while minimizing redundant focus on healthy regions.
occupy small areas in large CT volumes. The extreme foreground
In contrast to Al models, experienced radiologists can rapidly disregard irrelevant regions and con- centrate their diagnostic attention on potentially diseased regions during cancer screening. Typically, radiologists will glance at the entire CT volume, then focus on specific regions for precise diagno- sis (Wang et al.) 2025a). which inspires us to explore similar glance and focus strategy in AI models. In this work, we introduce GF-Screen, a Glance and Focus reinforcement learning frame- work for pan-cancer screening. GF-Screen adopts Glance model to localize the diseased regions at coarse level and a Focus model to precisely segment lesions at a finer level, with both models operating synergistically. Specifically, the Glance model crops group of sub-volumes from the whole CT volume and learns to select those containing lesions for the Focus model to segment. Given that the selecting operation is non-differentiiale for segmentation training, we propose em- play RL to optimize the Glance model by leveraging segmentation results from the Focus model. We carefully design yinfee-vet-effecttvv reward function for sub-volume selection, i.e., assigns high advantages to sub-volumes where the Focus model can accurately segment lesions and low advantages otherwise. To optimize the Glance model, we introduce novel group relative learning paradigm, which employs group relative comparison to prioritize high-advantage predictions and discard low-advantage predictions within sub-volume groups In this way, we effectively encour- age the model to focus diseased regions and discard redundant healthy regions, which not only improves efficiency but also reduces false positives.
To evaluate the effectiveness, we aggregate 5,117 CT scans from 23 public datasets for training and validation. Extensive experiments on 16 internal and 7 external datasets across 9 different lesion types highlight the superiority of GF-Sereen. By focusing diseased regions and discarding redun-
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dant healthy regions, GF-Screen reduces the computation costs by average of 5.7 times. Notably, GF-Screen leads the competitive MICCAI FLARE25 pan-cancer challenge validation leaderboard, which is one of the most representative challenges in this field. On the public validation leaderboard, GF-Screen surpasses the second-ranked algorithm (champion solution of FLARE22) by +25.6% DSC and +28.2% NSD, marking novel and practical breakthrough in pan-cancer screening.
RELATED WORKS
2.1
CANCER SCREENING
Al-driven cancer screening has witnessed rapid development in recent years, which plays an im-
portant role in improving patient survival rates (Cao al. 2023; Hu et al. 2025). Existing works primarily rely on segmentation models (Isensee et al. 2021 Wu et al. 2025b 2024). which segment lesions at the pixel level, precisely outlining the lesion sizes and positions for cancer screening. Despite their promising results, these approaches are often specialized to one single type of lesion. Moving forward, pan-cancer screening aims to employ one model for detecting diverse cancers, presenting greater challenges but promising potential for clinical practice (Chen et al. 2023; Ma
ctt al. 2024; Jiang et al. 2024 Lei et al. 2025; He et al.) 2024; [hen et al. 2025). Specifically. CancerUniT (Chen et al. 2023 employed query-based
(Cheng et al. 2022) to
segment eight types of lesions. ZePT (Jiang et al. 2024) focused on the zero-shot segmentation of
unseen lesion types. PASTA (Lei et al. 2025) pre-trained pan-cancer foundation model via tumor synthesis and segmentation.
Although decent performance has been demonstrated, existing works predominantly overlook the critical Bweground-backgroound imbalance in cancer screening. Lesions typically occupy small areas in large CT vol- umes (Miller et al. 1981: Bassi et al., 2025), making it challenging for models to focus on tiny diseased re- gions. As shown in Fig. 2 previous cancer screen-
Previous
GF-Screen
Steleg-wlsdew inforonce
Sliding wodlng-wtoodw invroree
ing methods (Isensee et al., 2021 Cao et al., 2023 Hu
al. 2025 Liu et al. 2023 Tang et al. [022] Chen
Glance model
et al. 2024a by Lei et al.) (025) generally employ slide-
Segmentation model
window inference on large CT volumes without dis- carding the redundant healthy regions, which not only impedes the inference efficiency but also increases false positives healthy regions. To this end, develop GF-Screen to address this significant challenge by mim- icking the glance-and-focut diagnostic strategy of radi- ologists, aiming to focus on diseased regions while ig- noring redundant healthy regions.
Reinfrrcmmen
Deoreosing offiiency
With
Leoging
tecroasing Aaso positivos
Focus model
Figure 2: Comparison between previous cancer screening methods and GF-Screen.
2.2 REINFORCEMENT LEARNING IN VISION PERCEPTION
RL has demonstrated remarkable effectiveness in vision perception tasks. Recent advanced vision- language models (Shen et al., 2025 Yue al. 2025 Wang al., 2025b Zhang al.) (025b) explored the vision reasoning ability via RL. However, most of these works employ RL to opti- mize the large language models. In pure vision tasks. RL can serve as a tool for adaptive visual search (Wang et al. 2020 Huang et al. 2022) Waag et al., 2021; Pardyl et al. 2024). For exam- ple, Wang al. (2020) proposed to train an actor-critic model (Haarnoja al. 2018) via Proximal Policy Optimization (PPO) (Schulman et al.) 2017) for discarding redundant patches in image clas- sification, improving efficiency without a significant performance drop. However, it fails to tackle dense image parsing tasks such as detection and segmentation, and also requires training extra value model for advantage estimation. In medical image analysis. Hao et al. (2022) proposed an RL method to imitate human visual search behavior for lesion localization in X-ray images. In this work, we extend the ofateoof-the-an RL technique to solve the specific challenges in pan-cancer screening, establishing novel and practical pioneer for the RI. applications in medical vision tasks.
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(a) Training
(b) Inference
sub-volumes
Relative Learning
Updade
Rewards
Dolo
Glance model
whiding-rhddww Inference
clesaiiication model to solect sub volumes with fesions
Segmendation
Glance
Random Grop
healthy regions
model
Focus model
aegmontation model segmeent sub- volumes
Focus discased
The overall framework of GF-Screen, including a Glance model to localize diseased re- gions and a Focus model to precisely segment lesions. (a) In the training stage, conduct seg- mentation on all sub-volumes and leverage the segmentation results to reward the Glance model via novel group-relative learning paradigm. (b) In the inference stage, dynamic number of sub-volumes classified as "with lesions" by the Glance model will be input to the Focus model for segmentation, where the redundant regions will be discarded.
Figure 3:
3 METHOD
3.1 TOWARDS PRECISE AND EFFICIENT PAN-CANCER SCREENING
In this work, we develop simple-yet-effectine framework, enabling precise and efficient pan-cancer screening. As illustrated in Fig. 3 GF-Screen comprises two key components: a Glance model for coarse-level localization and Foeus model for pixel-level segmentation. The Glance model is a lightweight classification model, which can efficiently categorize the cropped sub-volumes as either "with lesions" or "without lesions". The Focus model can deliver precise segmentation of lesions, providing important positions and sizes information for cancer screening. Both models allow seamless integration of advanced network architectures.
These two models operate synergistically during training and inference. During training, as shown in Fig. M(),, randomly cropped sub-volumes are fed into both models. The Focus model is super- vised by lesion masks in segmentation training, using typical combination of per-pixel binary cross-entropy loss and dice loss following previous medical image segmentation methods. The Glance model is trained via RL, where rewards are derived from the Focus model's segmentation results. In the inference stage (Fig. B(b),, the input CT volume is first divided into a group of sub- volumes using sliding-window approach as in previous methods. The Glance model then filters out healthy sub-volumes, forwarding only contonncntainingg sub- volumes to the Focus model for precise segmentation. In this way, GF-Screen improves inference efficiency by eliminating wasted computation on healthy regions, while also reducing false positives.
3.2 GLANCE AND FOCUS REINFORCEMENT LEARNING
In our framework, the most critical challenge is how effectively train the Glance model for dis- carding redundant healthy regions without compromising lesion recognition performance. Since we have lesion masks for supervision, straightforward way is degrade the lesion masks m as binary categories ("with lesions" or "without lesions") for each sub-volume then employing typical cross-entropy loss for classification training as follows:
G(v)). where o; denotes the selection outputs of the Glance model G. However, we observe that there are two fundamental shortcomings within this training approach:
CE(o,, y.) y (o.) (1 y.) (1 - log(o.)),
(1)
Lesions suffer from severe foreground- -awuundddd-bkroouddd imbalance in CT volumes, meaning most sub-volumes contain no lesions. This class imbalance causes the Glance model to overfit on negative cases, lowering its sensitivity to positives. Consequently. it fails to effectively select and forward diseased regions to the Focus model.
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As shown in Fig. due to random crop, many cesion-containing sub-volumes present chal- lenges caused by either partial inclusion or suboptimal viewing angles. If we simply de- grade the lesion masks as classification labels for training, the classification training of these sub-volumes would be significantly hampered.
Motivated by these challenges, we aim to develop more effective training paradigm for the Glance model. As shown in Fig. 4 experienced radiologists typically adopt an intelligent viewing strategy, i.e., they generally focus on the most diagnostically informative perspectives for accurate cancer diagnosis. GF-Screen emulates this clinical expertise in Al models, which developed to not only select the sub-volumes with lesions, but also prioritize the optimal views within the sub-volume groups for more precise segmentation.
To this end, propose leverage the segmeniation results of the Focus model supervise the Glance model. How- ever, it is worth noting that the selection operation of the Glance model is non- differentiable in the segmentation train- ing of the Focus model. To address this challenge, we introduce RL techniques for training the Glance model. Specifi- cally, in RL framework, the Glance model acts as a policy model to deliver not),
Challanging view for diagnosis
X
Optimal view for diegnosis
actions (select this sub-volume while the Focus model serves as a reward model reward the Glance model based on the segmentation results. In this way, effectively enable the Glance model to discard redundant regions and priori- tize the optimal diagnostic views that can yield better segmentation results.
Figure Illustration of sobumolume variation. The blue regions represent the challenging view with par- tial lesions and poor angles. While red regions indicate the optimal diagnostic view containing complete lesion information, generally with more precise segmentation results. Thus, propose leverage segmentation re- sults as reward signals for RL.
3.3 GROUP RELATIVE LEARNING FOR SUB-VOLUMES SELECTION
First, the Glance model acts as a policy model to deliver actions. The final layer of the policy model is 2-unit layer with softmax activation, outputting probability distribution over the two actions (select (1) or discard (0)) During training, the output o stochastically sampled from this softmax distribution. As with standard RL algorithms, can directly optimize the underlying probability distribution using the policy gradient, bypassing the non-differentiabiiiyy of the sampling step.
Reward design. Our reward function is derived from the segmentation results of the Focus model, offering a simple-yet-effective mechanism. Given a group of N cropped sub-volumes {u1, ?2,,.., UN }. the Focus model F generate segmentation prediction 8; for each sub-volume v;- The reward r; is binary and determined by the overlap between the predicted segmentation and the ground-truth lesion mask m,, Once the segmentation prediction 8; overlaps with the lesion mask my it returns rewards r, = 1; otherwise, it returns T 0:
r; (8, m,, + 0), 8; F(v.).
We have further explored using the segmentation DSC for more granular reward signal, but we observed that the performance is worse. We conclude that detection of the lesion presence is more important in the Glance model, while DSC varies significantly with lesion complexity (e.g.. types, sizes, and positions). Concretely, high segmentation DSC may simply reflect an "easy" sub- volume (e.g., one with clear boundaries or canonical orientation). Conversely, a low segmentation DSC might indicate challenging but clinically critical case (e.g., partial lesions viewing angles). Thus, to prioritize detection accuracy in the Glance model, we use the binary detection reward during RL training. Previous cancer screening works (Cao et al. 2023 Hu et al. 2025) also employed such detection methods to avoid discarding hard yet important cases that with lower segmentation scores, substantiating the reasonableness of reward design.
suboptimal
Group Relative Learning (GRL). Previous RL approaches for visual perception tasks WWag et al.) 2020 Huang et al. 2022) typically employ traditional policy optimization methods like PPO. How-
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Jan
Reference model
01
F/2
Giance model
O2
Reward function
F3
Group Computation
Az
CN
ON
FN
Ay
sub-volumes
selection ouliputs
rewards
advantagge
Figure 5: The Group Relative Learning (GRL) paradigm in GI-Screen. The Glance model G is trainable while the reference model Gyef is frozen. We first generate selection outputs from the input sub-volumes v, then use the reward function Eq. 2 to calculate the rewards r. Finally, we compute the relative advantages via the GAE function Eq.
ever, PPO requires training a large value model as a critic estimate the advantages, which brings a substantial memory and computational burden. In comparison, GRPO (Shao et al.) (024) foregoes the value model, instead using Large Language Models (LLMs) to generate group of candidate an- swers and estimate the relative advantages, effectively obviating the need for training an extra value model and achieving better performance. GRPO (Shao et al., 2024) has demonstrated remarkable success in recent L.Mss, where multiple candidate responses can be naturally generated. However, remains challenging to transfer GRPO to vision tasks due to the difficulty in generating a group of candidate results without LLMs.
Our GF-Screen naturally overcomes this limitation: the group of cropped sub-volumes readily provides comparable candidates. This unique feature enables to conduct reward comparison across sub-volumes for relative advantage estimation, eliminating the requirement of additional can- didate generation mechanisms and achieving seamless integration of group relative learning in GF- Screen. Moreover, the group relative advantage comparison effectively enables us to prioritize high- advantage predictions and discard low-advantage predictions within sub-volume groups. Without the usage of LLMs, GF-Screen effectively shifts the paradigm of GRPO from NLP to vision tasks.
The group relative learning paradigm in GF-Screen is shown in Fig. 5 We first calculate the relative advantages of sub-volume groups by Generalized Advantage Estimation (GAB), Specifically, we employ group computation to normalize the rewards by computing the mean and standard deviation (std) of rewards, subsequently deriving the advantage as:
4 Y; mean (r1, r,,,.,,, PN}
(3)
std(r1, P2....,
where A; represents the advantages of sub-volume In this way, we encourage the Glance model G select sub-voluu volumes with higher advantages within the group, which not only improves the inference efficiency but also reduces false positives by eliminating low-advantage predictions. Ulti- mately, we optimize the Glance model by maximizing the following objective JARL:
JGRL (0) E ~ G(v)
N
[min[$] A,, 82 A.] -DDD (EDKEGG aCE(o,
N
D De (Gl|Grer) = Gres(orlus)
log Gres(o.(us)
G(o:lu:)
G(oo(us)
61 G(v.)
G(vi) (vs)(
82 clip
- -,,1+++)
(raf(t))
where 0 represents the parameters of the Glance model G for updating. N is the number of sub- volumes, $1. 82, and are used to clamp the ratios following previous RL algorithms (Schulman et al. 2017 Shao et al. 2024). Gref represents the reference model (i.e. the initial Glance model). To maintnn traming stability (Schulman et al.) 2017: Shha et al., 2024) Feng et al., 2025). we freeze its parameters and update it at fixed steps following previous RI. methods. We also adopt the KI.

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Published as a conference paper at ICLR 2026
Table 1: Pan-cancer segmentation performance (DSC %) on internal datasets, Interactive segmen- tation methods rely manual prompts, which gain higher performance but cannot be applied to automatic screening. denotes that this model cannot apply to this lesion type.
Method
Lung
Lung
Pleural
COVID-19
Liver
Pancreas
Kidney
Adreno.
Colon
Average (per type)
nodule
effusion
infection
tumer
tumor
carcinoma
Interactive orgmentaatio
(require manual prompts)
SegVol (poinl+texi)
68.9
71.8
68.3
90.5
69.7
(text)
34.2
67.9
ULS (point)
76.5
65.1
57.9
82.9
68.3
LosionLocator (point)
77.1
76.8
67.9
89.1
75.3
Pon-cancer segmentation
screening)
anUNet
50.4
48.1
36.8
61.5
61.2
35.9
68.3
86.6
30.6
53.3
SwinUNETR
47.8
43.6
26.3
58.5
$7.0
27.6
70.8
83.0
21.0
48.6
3D UX-Net
42.4
10.2
56.1
16.3
62.3
82.5
Cliv--drveen
46.7
36.8
22.8
63.1
23.7
67.0
87.5
20.2
47.6
TNets
51.2
36.7
17.9
60.8
$9.2
29.9
64.7
87.7
14.5
46.9
UniMiSS+
49.1
40.3
23.6
62.0
55.5
22.1
64.1
83.6
12.1
45.9
VeCo
SuPreM
52.5
48.0
38.5
64.1
40.2
71.2
88.0
21.9
54.4
PASTA
52.1
48.2
23.3
64.7
66.2
30.8
72.1
88.6
29.3
52.8
Table 2: Pan-cancer detection performance (F1-Score %) on internal datasets. We did not compare the interactive-basee methods since the prompt will already provide whether this sean contains le- sions not. We report the average results for each CT scan.
Method
Lung
Lung nodule
Pleural effusion
COVID-19
Liver
Kidney
Averuge (per type)
infection
tumor
DnUNet
92.0
92.8
95.5
97.5
72.2
90.2
SWinUNETR
92.5
88.9
72.0
92.5
UXt
89.7
84.6
35.7
86.3
CLIP-driven
90.2
88.9
96.7
46.7
UNet
90.2
79.9
96.2
95.5
87.8
93.3
64.7
89.1
UniMiSS+
91.1
79.9
95.5
51.6
86.7
VoCo
91.1
86.3
92.8
96.2
97.8
98.8
93.3
82.0
92.2
SuPrcM
92.0
86.3
92.8
97.4
96.7
85.8
97.5
93.3
72.2
90.4
PASTA
89.7
86.3
79.9
96.2
96.7
85.8
97.5
93.3
72.2
88.6
GF-Sereen
96.4
91.9
96.6
100.0
98.2
95.1
100.0
100.0
85.0
95.9
penalty DxL to regularize the divergence between G and Gref- and 3 denotes the coefficient of DKL.- The Dxr. term follows prior work (Shao et al. 2024), which employs a second-order Taylor expansion approximation of the standard KI. divergence. A detailed derivation is provided in the appendix. We further add the classification loss CE as Eq. [ the objective. Specifically, we employ small coefficient a avoid the model overfitting to the negative class as discussed above. Overall, the optimization objective of the Glance model is to maximize the reward, i.e., minimize the loss LGAL JGNL(O), Our RL training operates synergistically with segmentation training, enabling an end-to-end framework for pan-cancer screening.
4
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Our training and validation dataset includes total of 5,117 scans from 23 public datasets across lesion types, as shown in Appendix Table AT datasets, then aggregate all the training sets universal training. To demonstrate that the effec- tiveness of GF-Screen does not rely on -ppecic biases, we involve 7 external datasets in evaluation, which are unseen in training. For the FLARE25 dataset, evaluate the performance the public validation leaderboard, making our work more trustworthy and easier to adopt.
fixed train and val splits for the 16 internal
In GF-Screen, adopt a lightweight 3D ResNet- 18 (Chen et al. 2019 He et al. 2016) the Glance model. and SwinUNETR (Hatamizadeh et al. 2021) as the Focus model for fair comparisons with state-of-the-an models (Liu al. 2023 Wu 2025a Li Liet al. 2024). Thus, SwinUNETR can be

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Published as a conference paper at ICLR 2026
Table 3: Pan-cancer segmentation performance (DSC %) on external datasets. The used external datasets are described Table AT
Table 4: The false positive rates (%) on three in- ternal and external datasets. Lower false positive rates represent higher performance.
Method
Rider
Corona
IR.
Avg.
Method
CHAOS
TCIA.
Atlas
Avg.
nnUNet
35.3
60.7
51.0
49.0
nnUNet
40.0
21.2
29,9
30.4
Swin.
25.6
57.7
47.2
43.5
Swin.
40.0
41.3
61.1
47.5
UX-Net
13.5
33.6
39.4
28.8
UX-Net
40.0
46.3
58.6
48.3
CLIP.
27.6
50.6
51.1
43.1
CLIP
40.0
45.0
58.6
47.9
Trans.
17.6
50.6
49.0
39.1
Trans.
45.0
76.3
66.2
62.5
UniM.
20.1
45.7
43.5
36.4
UniM.
55.0
65.0
63.3
61.1
VoCo
31.3
55.8
56.5
47.9
VoCo
40.0
28.8
56.5
41.8
SuPreM
29.1
54.2
53.9
45.7
SuPreM
40.0
32.5
43.3
38.7
PASTA
24.2
58.1
52.7
45.0
PASTA
45.0
43.8
38,9
42.6
GF-Screen
38.3
64.3
59.7
54.1
GF-Screen
10.0
13.8
22.9
15.6
seen the baseline. For the GRL hyperparameters, empirically set N 16, 0.1, 0.1, and / = 0.01 following previous GRPO settings. The reference model updated per epoch, which the reference model for the
means that the Glance model trained in the current epoch will next epoch. The coefficients of GRL loss for the Glance model and segmentation loss Leeg (typical Dice-CE loss) for the Focus model are be equal. The overall loss function is as follows:
LGRL + Leeg -GRRL( + LDice-CE.
(5)
All the training experiments are conducted on one NVIDIA A800 (80G) GPU, and the inference can be conducted within 16G GPU storage. We use AdamW as the optimizer with learning rate of 3e-4, using cosine decay scheduler. We adopt a batch size of 4, and for each volume, we crop sub-volumes, (a total of 16 sub-volumes) In pre-processing, following the settings of the FLARE25 challenge, we split the dataset into chest and abdomen CT scans, then clipped the Hoplield Unit (HU) value (window adjustment) and normalized them [0. 1] as previous methods. For chest CT, the [min, max] of clipped HU set to [-900, 650j, and [-175, 250) for abdomen CT. The size of each sub-volume is set as [96, 96, 64). During inference, simply a sliding following previous methods. No post-processing techniques are used.
sliding-window window approach
4.2 PAN-CANCER SEGMENTATION AND DETECTION
We first evaluate the performance pan-cancer segmentation as shown in Table [ Specifically, GF-Screen achieves an average of 60.8% DSC across lesion types, surpassing the second-best method (Wu et al. 2025a) by 4.7%. We also present the results of interactive-based methods. Al- though these methods can achieve higher performance with the guidance of manual prompts, our model can also obtain competitive results on lung nodules, COVID-19, pancreas tumors, kidney tumors, and adrenocortical carcinoma. The detection F1-Scores are shown Table where GF- Screen achieves 95.9% F1-Score and surpasses previous methods by clear margin.
The segmentation results on three external datasets, Rider (Aerts et al. 2014). Corona PPiva 2020
and IRCADb (Soler et al. 2010), are shown in Table 3 across lung tumors, COVID-I9, and liver tumors. It can be seen that GF-Screen achieves superior performance in external validation, with 54.1% DSC average and surpasses the second-best nnUNet (Isensee al. (02T) by 5.1 %. We further present the false positive results on three healthy datasets in Table Although some seg- mentation models like VoCo (Wu al. (025a)), SuPreM (Li et al., 2024). and PASTA (Lei al.) 2025) can yield competitive segmentation results, they suffer from high false positives on healthy datasets. We observe that is caused by the overhead focusing on redundant healthy regions. GF- Screen is developed to tackle this challenge by discarding low-advantage predictions. For example, compared with SuPreM (Li et al. 2024), GF-Screen achieves 23.1 % lower false positives.
4.3 SUPERIOR PERFORMANCE ON PAN-CANCER CHALLENGE
We first evaluate on the FLARE23 pan-cancer dataset (Ma et al., 2024) in Table GF-Screen yields 56.7% DSC and surpasses previous methods by clear margin. We further evaluate the average
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Table 5: Performance on
Table 6: Average inference
Table 7: Evaluation on MICCAI FLARE25 validation leaderboard, compared with the champion solu- tion of FLARE24 (Isensee et al.
FLARE23 dataset.
duration (seconds/per scan).
Method
DSC
Method
Duration
anUNet
43.7
nnUNet
136
Swin.
41.5
Swin.
114
2021 Huang. 2024) and several
UX-Net
20.1
UX-Net
102
comparison methods.
CLIP
28.3
CLIP
109
Method
DSC
NSD
Trans.
27.2
Trans.
135
FLARE24 champion
UniM.
25.1
UniM.
128
nnUNet (Huang 2024)
33.0
24.0
VoCo
50.6
VoCo
114
Swin.
30.7
23.9
SuPreM
SuPreM
114
VoCo
47.5
41.0
PASTA
34.6
PASTA
197
GF-Screen
58.6
52.2
GF-Screen
56.7
GF-Sereen
28
Table 8: We report the classification sensitivity and specificity of the Glance model.
Method
Sen.
Spe.
Ratio(%))
GFLOP
DSC
SwinUNETR
100
12155
48.6
Training Spache
286
203
GF-Screen
97.7 75,9
16.7
2164
60.8
Figure 6: RL loss curve in GRL training.
inference duration results in Table 6 since efficiency plays important role in the real-world de- ployment of large-scale cancer screening (Ma et al., (002)). can be seen that most comparison methods suffer from long inference duration and fail balance efficiency with accuracy. This be- cause in these models, huge computation costs will be wasted in redundant regions, while GF-Screen effectively addresses this challenge by discarding redundant regions.
The results on the FLARE25 validation leaderboard are in Table Due to the limitation of submis- sion times, we only compare several comparison methods (Isensee et al., 202T]) Wu al.) 2025a
Hatamizadeh et al. 2021). Specifically, [Huang (2024) modified nnUNet (ssnssee et al., 202 I) to balance the efficiency and accuracy, which has won the champion of FLARE24 (Ma et al. 2024). GF-Screet surpasses the champion solution of FLARE24 by large margin (+25.6% DSC and +28.2% NSD), highlighting a novel and practical breakthrough in pan-cancer screening.
4.4 ABLATION STUDIES
We first evaluate GF-Screen in selecting diseased regions and discarding healthy regions. We crop the CT volumes into vol-volumes using a sliding window approach. Then, we evaluate the sensitiv- ity and specificity results of the Glance model in classifying these sub-volumes. On average across
Table 9: Ablation studies of RL training. We report DSC(%) on the FLARE23 dataset. We report the ratio(%) to indicate the proportion of sub-volumes selected by the Glance model.
Method
DSC
ratio
SWinUNETR Hatamizadeh et al. 2021
41.5
100
Without RL Glance model classification training
Cross-entropy loss (Eq.
37.6
3.1
Balanced cross-entropy loss
37.8
Focal loss (Lin et al.
2017
36.5
4,0
Hard-Negative Sampling Felzenszwall et al.
2009
35.0
3.3
OHEM (Shrivastava et al.
2016
39.5
7.2
With il. Glance model classificatin training
Actor-Critic PPO (Wang al. 2020 Huang
2022
Schulman et al. 2017
24.5
51.6
Group Relative Learning (DSC as rewards)
43.2
21.3
Group Relative Learning (Reward function Eq.
without classification loss oCE
53.1
23.0
Group Relative Learning (Reward function Eg
with classification loss aCE)
56.7
16.7

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Published as a conference paper at ICLR 2026
Table 10: Ablation studies of Table 11: Ablation studies of Table 12: Ablation studies of the
efficiency.
segmentation backbone.
Glance model backbone and N.
Method
GFL.OP
Method
DSC
Glance model backbone
DSC
3D ResNet-18 (32M)
56.7
baseline
12155
UNETR
16.2
3D ResNet-34 (63M)
56.5
GF-Screen
+GF-Screen
27.3
3D UNet (31M)
56.0
w.o. discard
13726
TransUNet
27.2
discard
2164
+GF-Screen
34.8
N in group relative learning
DSC
nnNNet
43.7
N 4
45.9
+GF-Screen
45.6
N = 8
51.6
SwinUNETR
41.5
N 16
56.5
+GF-Screen
56.7
N 32
56.4
all validation datasets, there are 87.6% without lesions and 12.4% with lesions in the sub-volumes. We show the ratio of preserved sub-volumes selected by the Glance model:
number of selected sub-volumes number of total cropped sub-volumes
ralio
As in Table GF-Screen selects diseased sub-volumes with high sensitivity (97.7%). By discarding 83.3% redundant sub-volumes (preserved ratio 16.7%), GF-Screen reduces the computation cost by an average of 5.7 times. We further analyze the loss curves of GRL, as shown in Fig. 6 We observe that the resulting curves closely align with those in prior GRPO literature, demonstrating the robustness of our training process. More sensitivity analyses of training are in Appendix Fig. A6
We further conduct ablation studies of RL training in Table We report the results on the FLARE233 pan-cancer dataset. First, as discussed in Section 3.2 we observe that direct usage of cross-entropy loss (Eq. [ for classification training yields worse results. We observe that the severe foreground- background class imbalance causes the model to collapse into predicting the negative class. As can be seen, the preserved ratio is decreased 3.1 % in this case, which means the Glance model tends to predict the negative class in classification. We observe that simply changing the criterion loss or using hard-sample mining (Felzenszwalb et al.) 2009; Shrivastava al. (016) cannot effectively improve the performance. We conclude that stronger classification baselines still rely on the coarse volume-level classification labels, which cannot effectively address the balance. These results motivate us to introduce more precise supervision for training. Thus, propose to leverage the fine-grained segmentation result as rewards for RL training, which can pro- vide more precise supervision for the Glance model.
im-
Second, we explored the classical PPO algorithm (Schulman et al. 2017) to optimize the Glance model. This approach requires training additional value model to calculate the rewards. We follow the settings of previous works (Wang et al. 2020 Huang et al., 2022) to train this value model, and optimize the Glance model using the PPO algorithm. However, result is bad, and the Glance model cannot stably converge to predict the classes of the sub-volumes. To this end, we highlight that more advanced RL methods should be used.
observe that the
we evaluate the effectiveness of GRL. We first explore leveraging the DSC results as rewards instead of the binary detection reward in Eq. As shown in Table this approach achieves 43.2% DSC on FLARE23. The performance is slightly better than the baseline, and the efficiency is sig- nificantly improved (ratio 21.3%, 78.7% sub-volumes are discarded). We then explore the usage of binary detection reward as Eq. and the performance is further improved. We conclude that for the Glance model, accurate detection more important. We further add the classification loss aCE in JGRL (0), which can further improve the performance.
Third,
We conduct ablation studies to evaluate the efficiency in Table 10 Although the classification pro- cess introduces minor computational overhead, the discarding of redundant healthy regions drasti- cally reduces segmentation costs. We evaluate multiple network architectures in Tables [T and 12 To balance efficiency and accuracy, employ 3D ResNet-18 18 the Glance model. We further
evaluate the settings of N in group relative learning in Table [2] Empirically, we set N = 16 in the experiments. More visualization results shown in the Appendix.
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5 ACKNOWLEDGMENTS
This work was supported by the Hong Kong Innovation and Technology Commission (Project No. GHP/006/22GD and ITCPD/17-9), HKUST (Project No. FSIII), HKUST-HKUST(GZ) Cross- Campus Collaborative Research Scheme (Project No. C036), and Guangdong Provincial Depart- ment of Science and Technology's s '1+1+1' Joint Funding Program for Guangdong- Kong Universities. We highly appreciate the reviewers for their valuable comments on our manuscript, which have significantly improved our manuscript.

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Published as a conference paper at ICLR 2026
REFERENCES
Hugo JWL Aerts, Emmanuel Rios Velazquez, Ralph TH Leijenaar, Chintan Parmar, Patrick Gross- mann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Kains, Derek Rietveld, et al. Decoding tumour phenotype by noninvasive imaging using quantitative radiomics ap- proach. Nature Communications, 5(1):4006, 2014.
AA Ahmed et al. Radiomic mapping model for prediction of ki-67 expression in adrenocortical carcinoma. Clinical Radiology, 75(6):479-e177 2020.
Natália Alves et al. The PANORAMA Study Protocol: Pancreatic Cancer Diagnosis- Radiologists
Meet AI, February 2024. URL httts:://ti..oooo org/0 52..5228112 wwwp///eoo.... http:/eeoooo.......... 10599559
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneherger, Ronald M Summers, al.
The medical segmentation decathlon. Nature Communications,, 13(1):4128, 2022.
Samuel G Armato III, Geoffrey McI.ennan, Lue Bidaut, Michael F McNitt-Gray, Charles Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al. The lung image database consortium (lide) and image database resource initiative (idri): a completed reference database of lung nodules ct scans. Medical physics, 38(2):915-931, 2011.
Oscar Arrieta et al. Radical aggressive treatment among non-small cell lung cancer patients with malignant pleural effusion without extra-thoracic disease. Journal of Thoracic Disease, 11(2): 595, 2019.
Shaimaa Bakr et al. A radiogenomic dataset of non-small cell lung cancer. Scientific data, 5(1):1-9, 2018.
Krzysztof Bartnik et al. Waw-tace: A hepatocellular carcinoma multiphase et dataset with segmen- tations, radiomics features, and clinical data. Radiology: Artificial Intelligence, 6(6):e240296, 2024.
Pedro RAS Bassi, Mehmet Can Yavuz, Kang Wang, Xiaoxi Chen, Wenxuan Li, Sergio Decherchi, Andrea Cavalli, Yang Yang. Alan Yuille, and Zongwei Zhou. Radgpt: Constructing 3d image-text tumor datasets. In Proceedings of the IEEF/CVF international conference on computer vision, 2025.
Freddie Bray, Mathieu Laversanne, Hyuna Sung, Jacques Ferlay, Rebecca L. Siegel, Isabelle Soer- jomataram, and Ahmedin Jemal. Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 74 (3):229-263, 2024.
Kai Cao, Yingda Xia, Jiawen Yao, Xu Han, Lukas Lambert, Tingting Zhang, Wei Tang, Gang Jin, Hui Jiang, Xu Fang, et al. Large-scale pancreatic cancer detection via non-contrast et and deep learning. Nature Medicine, 29(12)3033333443 2023.
Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, al. Cancerunit: Towards a single unified model for effective detec- tion, segmentation, and diagnosis of eight major cancers using large collection of ct scans. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21327-21338, 2023.
Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, al. Transunet: Rethinking the u-net architecture design for med- ical image segmentation through the lens of transformers. Medical Image Analysis, 97:103280, 2024a.
Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong, Alan Yuille, Chen Wei, and Zongwei Zhou. To- wards generalizable tumor synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024b.
12

---

Published as a conference paper at ICLR 2026
Qi Chen, Xinze Zhou, Chen Liu, Hao Chen, Wenxuan Li, Zekun Jiang. Ziyan Huang, Yuxuan Zhao, Dexin Yu, Junjun He, et al. Scaling tumor segmentation: Best lessons from real and synthetic data. In Proceedings of the IEEE/CVF Internationl Conference Computer Vision, pp. 24001- 24013, 2025.
Sihong Chen, Kai Ma, and Yefeng Zheng. Med3d: Transfer learning for 3d medical image analysis. arXiv preprini arXiv: arXiv:1904.000 1904490044..,... 2019.
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked- attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1290-1299 2022.
MJJ de Grauw, E Th Scholten, Ewoud J Smit, Matthieu JCM Rutten, M Prokop, B van Ginneken, and Alessa Hering. The uls23 challenge: A baseline model and benchmark dataset for 3d universal lesion segmentation in computed tomography. Medical Image Analysis, 102: 2025.
Yuxin Du, Fan Bai, Tiejun Huang, and Bo Zhao. Segvol: Universal and interactive volumetric medical image segmentation. Advances in Neural Information Processing Systems, 37:1 10746- 110783, 2024.
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discrininatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3279)1122- 2009.
Wenfeng Feng, Penghong Zhao, Guochao Jiang, Chuzhan Hao, Yuewei Zhang, and Hao Wang. Pypo: Pre-estimated basue policy optimization for agentic reasoning, 2025. URL https:
//axxiv.org/abb//2500000 21104
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with stochastic actor. In International confer- ence on machine learning. pp. 1861-1870. Pmlr, 2018.
Degan Hao, Dooman Arefan, and Shandong Wu. Incorporate radiograph-reading behavior and knowledge into deep reinforcement learning for lesion localization. In Medical Imaging 2022: Computer-Aided Diagnosis, volume 12033, pp. 258-263. SPIE, 2022.
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang. Dong Yang, Holger R Roth, and Daguang Xu. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In International MICCAI brainlesion workshop, pp. 272-284. Springer, 2021.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference computer vision and patlern recognition, pp- 770-778, 2016.
Sunan He, Yuxiang Nie, Hongmei Wang, Shu Yang, Yihui Wang. Zhiyuan Cai, Zhixuan Chen, Yingxue Xu, Luyang Luo, Huiling Xiang, et al. Gsco: Towards generalizable ai in medicine via generalist-specialis collaboration. arXiv preprint arXiv:2404. 15127, 2024.
Nicholas Heller, Fabian Isensee, Dasha Trofimova, Resha Tejpaul, Zhongchen Zhao Huai Chen, Lisheng Wang, Alex Golts, Daniel Khapun, Daniel Shats, et al. The kits21 challenge: Auto- matic segmentation of kidneys, renal tumors, and renal cysts in corticomedulllry-phass ct. arXiv preprint ar 2rXhv:2307.01984 2023.
Can Hu, Yingda Xia, Zhilin Zheng, Mengxuan Cao, Guoliang Zheng, Shangqi Chen, Jiancheng Sun, Wujic Chen, Qi Zheng, Siwei Pan, et al. Ai-based large-scale screening of gastric cancer from noncontrast et imaging. Nature Medicine, pp. 1-9, 2025.
Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, and Shiji Song. Glance and focus networks for dynamic visual recognition. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 4564))4605-465 2022.
Ziyan Huang. Efficient whole-body tumor segmentation with a 5.6 m parameter 3d u-net. In MICCAI Challenge on Fast and Resoure Semi-supervias Abdominal Organ Segmentation, pp. 13- 22. Springer, 2024.
13

---

Published as a conference paper at ICLR 2026
Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu- net: a self-configuring method for deep learning-basc based biomedical image segmentation. Nature Methods, 18(2):203-22 211, 2021.
Yankai Jiang, Zhongzhen Huang, Rongzhao Zhang, Xiaofan Zhang, and Shaoting Zhang. Zept: Zero-shot pan-tumor segmentation via query-disentangling and self-prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1 11386-11397, 2024.
Yankai Jiang, Wenhui Lei, Xiaofan Zhang, and Shaoting Zhang. Unleashing the potential of vision- language pre-training for 3d zero-shot lesion segmentation via mask-attribns alignment. In The Thirleenth International Conference on Learning Represenlutions (ICLR), 2025.
Yuming Jiang et al. Predicting peritoneal recurrence and disease-frree survival from ct images in gastric cancer with multitask deep learning: a retrospective study. The Lancet Digital Health, (5):e340-e33) 2022.
A. Emre Kavur, N. Sinem Gezer, Mustafa Bariş, Sinem Aslan, Pierre- Conze, Vladimir Groza, Due Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachi- nov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde Bozdağı Akar, Gözde Ünal, Oğuz Dicle, and M. Alper Selver. CHAOS Challenge com- bined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis, 69: 60:90,,,0, April 2021. ISSN 13115. doi: https://doiiorg/10.11111 mww.aa.......m....0.00.....0 200...1050000000000000 URL http: //www Sc incedi rect. com
84 5230031 45
Ho Hin Lee, Shunxing Bao, Yuankai Huo, and Bennett A Landman. 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. In The Eleventh International Conference Learning Representations (ICLR), 2023.
Wenhui Lei, Hanyu Chen, Zitian Zhang, Luyang Luo, Qiong Xiao, Yannian Gu, Peng Gao, Yankai Jiang, Ci Wang, Guangtao Wu, et al. A data- efftc-efficeet pan- pan-tumor foundation model for oncology ct interpretation. arXiv preprint arXiv:2502.06117 2025.
Wenxuan Li, Alan Yuille, and Zongwei Zhou. How well do supervised models transfer 3d image segmentation. In The Twelfth International Conference on Learning Representations, volume 1, 2024.
Tsung-Y Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp- 3980-2988, 2017.
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou. Clip-driven universal model for organ segmenta- tion and tumor detection. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 21 152 21 164, 2023.
Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Mae, Adamo Young, Cheng 7hu, Xin Yang. Kangkang Meng, Ziyan Huang, et al. Unleashing the strengths of unlabelled data in deep learning- assisted pan-cancer abdominal organ quantification: the flare22 challenge. The Lancet Digital Health, 6(11):e815-e82) 2024.
Scott Mayer McKinney, Marcin Sienick, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg Corrado, Ara Darzi, et al. International evaluation of an ai system for breast cancer screening. Nature, 577(7788):89 94, 2020.
AB Miller, BFAU Hoogstraten, MFAU Staquet, and A Winkler. Reporting results of cancer treat- ment. cancer, 47(1):207-214, 1981.
Ali Morshid, Khaled M Elsayes, Ahmed M Khalaf, Mohab M Elmohr, Justin Yu, Ahmed Kaseb, Manal Hassan, Armeen Mahvash, Zhihui Wang, John D Hazle, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radi- ology: Artificial Intelligence, 115)22 2019.
14

---

Published as a conference paper at ICLR 2026
O Paiva. Helping radiologists help people in more than 100 countries!- coronavirus CORONACASES. ORG. Ed., ed, 11, 2020.
Adam Pardyl, Michal Wronka, Maciej Wolezyk, Kamil Adamczewski, Tomasz Trzciński, and Bar- tosz. Zieliński. Adaglimpse: Active visual exploration with arbitrary glimpse position and scale. In European Conference Computer Vision, pp. 129.129. Springer, 2024.
Perry J Pickhardt, Peter M Graffy, Ryan Zea, Scott Lee, Jiamin Liu, Veit Sandfort, and Ronald M Summers. Automated ct biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. The Lancet Digital Health, 2(4) 192-c200, 2020.
Perry J Pickhardt, Ronald M Summers, John W Garrett, Arun Krishnaraj, Sheela Agarwal, Keith Dreyer, and Gregory N Nicola. Opportunistic screening: radiology scientific expert panel. Radi- ology, (07())))2222244,2222 2023.
Chongyu Qu, Tiezheng Zhang. Hualin Qiao, Yucheng Tang, Alan L Yuille, Zongwei Zhou, et al. Abdomenatlas-8k. Annotating 8,000 volumes for multi-organ segmentation three weeks. Advances in Neural Information Processing Systems, 36, 2023.
Maximilian Rokuss, Yannick Kirehhoff, Seval Akbal, Balint Kovaes, Saikat Roy, Constantin Ulrich, Tassilo Wald, Lukas T Rotkopf, Heinz-Peter Schlemmer, and Klaus Maier-Hein. Lesionlocator: Zero-shot universal tumor segmentation and tracking in 3d whole-body imaging. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 30872-30885, 2025.
Holger Roth, Amal Farag, Evrim B. Turkbey, Le Lu, Jiamin Liu, and Ronald M. Summers. Data from pancreas-ct, /ttppp////aaalloooo////////////1 2016 tNB1kGBU 2016. The Cancer Imaging Archive.
Holger R Roth, Ziyue Xu, Carlos Tor-Diez, Ramon Sanchez Jacob, Jonathan Zember, Jose Molto, Wenqi Li, Sheng Xu, Baris Turkbey, Evrim Turkbey, et al. Rapid artificial intelligence solutions in pandemic- the covid-19-20 lung ct lesion segmentation challenge. Medical Image Analysis, 82:102605, 2022.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv: 2017.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- cal reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, et al. Vlm-rl: A stable and generalizable rl rl-style large vision-language model. arXiv preprint arXiv:2504 07615, 2025.
Yiwey Shieh, Martin Eklund, George F Sawaya, William C Black, Barnett S Kramer, and Laura Esserman. Population-base screening for cancer: hope and hype. Nature reviews Clinical on- cology, 1309)5500-565 2016.
Abhinav Shrivastava, Abhinay Gupta, and Ross Girshick. Training region-based object detectors with online hard example mining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 761-769, 2016.
Lue Soler, Alexandre Hostettler, Vincent Agnus, Arnaud Charnoz, Jean-Baptiste Fasquel, Johan Osswald, Mourad Bouhadjar, and Jacques Marescaux. 3d image Moreau, comparison of algorithm database. URL: https://www. ircad. fr/research/data- construction for Recognition, pp. httfll/ewrggmmeaattioiodd--a---a 2010.
Yucheng Tang, Dong Yang, Wengi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattenn 2022.
15

---

Published as a conference paper at ICLR 2026
Lehan Wang. Haonan Wang, Honglong Yang, Jiaji Mao, Zehong Yang, Jun Shen, and Xiaomeng Li. Interpretable bilingual multimodal large language model for diverse biomedical tasks. In International Conference Learning Representations, 2025a.
Yaoting Wang, Shengqiong Wu, Yuecheng Zhang, Shuicheng Yan, Ziwei Liu, Jiebo Luo, and Hao Fei. Multimodal chain-of-thought reasoning: A comprehensive survey. arXiv preprint arXiv:2503.12605 2025b.
Yulin Wang, Kangchen Lv, Rui Huang. Shiji Song, Le Yang, and Gao Huang. Glance and focus: dynamic approach to reducing spatial redundancy in image classification. Advances in Neural Information Processing Systems, 33:2432-2444, 2020.
Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, and Gao Huang. Adaptive focus for efficient video recognition. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 16249-16258, 2021.
Linshan Wu, Jiaxin Zhuang, and Hao Chen. Voco: A simple-yyt-effective volume contrastive learn- ing framework for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22882, 2024.
Linshan Wu, Jiaxin Zhuang, and Hao Chen. Large-scale 3d medical image pre-training with geo- metric context priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025a.
Linshan Wu, Jiaxin Zhuang, Yanning Zhou, Sunan He, Jiabo Ma, Luyang Luo, Xi Wang, Xuefeng Ni, Xiaoling Zhong, Mingxiang Wu, et al. Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition. Nature Communications, 16( 16(1):11053, 2025b.
Yutong Xie, Jianpeng Zhang, Yong Xia, and Qi Wu. Unimiss+: Universal medical self-supervieed learning from cimesndimeniiona unpaired data. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2024.
Ke Yan, Xiaosong Wang. Le Lu, Ling Zhang, Adam Harrison, Mohammadhadi Bagheri, and Ronald M Summers. Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large-scale lesion database. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9261-9270, 2018.
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang. Shiji Song. and Gao Huang. Does re- inforcement learning really incentivize reasoning capacity in llms beyond the base model? arXiv preprint arv::22004. 13837, 2025.
Hantao Zhang. Yuhe Liu, Jiancheng Yang. Shouhong Wan, Xinyuan Wang. Wei Peng, and Pascal Fua. Lefusion: Controllable pathology synthesis via Thirteenth International Conference on Learning Representations (ICLR), 2025a.
diffusion models. In The
Xintong Zhang, Zhi Gao, Bofei Zhang, Pengxiang Li, Xiaowen Zhang, Yang Liu, Tao Yuan, Yuwei Wu, Yunde Jia, Long-Chur Zhu, et al. Chain-of focus: Adaptive visual search and zooming for multimodal reasoning via rl. arXiv preprint arXiv:2505. 15436. 2025b.
Yucheng Zhang, Anastasia Oikonomou, Alexander Wong, Masoom A Haider, and Farzad Khalvati. Radiomics-based prognosis analysis for non-small cell lung cancer. Scientific reports, 7(1):6339, 2017.
Ziheng Zhao, Yao Zhang, Chaoyi Wu, Xiaoman Zhang, Xiao Zhou, Ya Zhang, Yanfeng Wang, and Weidi Xie. Large-vocabulary segmentation for medical images with text prompts. npj Digital Medicine, 8(1):566, 2025.
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6 APPENDIX
6.1 EXPERIMENTAL SETTINGS
Table A1: The datasets used for training and validation in pan-cancer screening. For the Atlas dataset (Bassi et al., (025), we select the healthy cases based on the provided reports and ensure they do not overlap with other datasets. Overall, there are total of 5,117 CT scans from 16 internal and external datasets covering 9 different types lesions.
Dataset
Tumor/Leeion Types
Scans (Train/ Vall
Internal datasets
MSD06-Lung (Antonelli et al.
2022
Lung tumor
46/17
NSCLC- Radiogenomics [Bakr
2018
Lung tumor
68/20
Radiomma (Zhang et
2017
Lung tumor
323/92
LIDC AAmato III et al.
2011
Lung nodule
826/184
(Arritta et al.
2019
Pleural effusion
63/15
COVID-19 Roth ct al. 2022)
COVID- 19 infection
158/41
MSD03-Liver Antonelli ct al. 2022
Liver tumor
95/23
ASDO8-Hepatte (Antonelli et al. 2022
Liver tumor
248/55
WAWTACE (Bartnik
2024
Liver tumor
172/47
HCC Morshid 2019
Liver tumor
60/14
MSD07-Pancrean (Antonelli et al.
2022
Pancreas tumor
226/55
PANORAMA (Alves al.
2024
Panereas tumor
376/106
KiTS23
Heller et al.
2023
Kidney turor
389/99
Adrenal
Ahmed ct al.
2020
Adrenocortical carcinoma
44/8
MSDI0-Colds Coloon Antonelli et al.
2022
Colon tumor
103/23
Atlas Bassi et al.
2025)
Without lesions
628/157
External datasers
Rider (Aerts 2014
Lung tumor
0/56
Corona Paiva 2020
COVID-19 infection
0/10
IRCADb Soler et al.
2010)
Liver tumor
0/20
CHAOS Kavur al.
2021
Without lesions
0/20
TCIA- Pancccass (Roth et al.
2016
Without lesions
0/80
FLARE23 (a et al.
2024)
Pan-cancer
0/50
FLARE25 (val&test) (Ma et al.
2024
Pan-cancer
0/100
Total
3,825/1,292 (5,117)
Datasets. We list the used datasets in Table[Al Metadata of the used datasets shown in Table A2 including scanner, contrast phase, and voxel size. The information about scanners and contrast phases is collected from the public links of the used datasets. Specifically, some public datasets did not disclose scanner manufacturer information or lacked the details of contrast phase information. The leaderboard comparison is conducted under equal conditions. The official FLARE challenge holds a list of datasets for participants to conduct training. No external data is allowed.
Comparison methods. We compare state-offtth-art medical image segmentation methods, includ- ing nneNet (Isensee et al., (02T). SwinUNETR (Hatamizadeh et al. (202T) , 3D UX-Net (Lee et al., 2023). CLIP-driven (Liu et 2023). TransUNet (Chen et al. (0024)), UniMisst (Xie al.) (024)) VoCo (Wu et al., (225a). SuPreM (Li et al. 2024). and PASTA (Lei et al., 2025). which can be leveraged for automatic cancer screening. Specifically, UniMisst, VoCo, SuPreM, and PASTA are pre-trained models. We fully train all these models on our datasets and report the results. One related work CancerUnit (Chen et al., 2023) is not publicly available, thus we cannot compare it. We further compare several interactive segmentation methods, including Seg (Du et al.) 2024). SAT (Zhao jtt al. 2025). ) ULS (de Grauw et al. 2025), and LesionLocator (Rokuss et al. 2025). which require manual prompts like points, boxes, text descriptions. Note that interactive methods rely man- ual prompts, which gain higher performance but cannot be applied to automatic screening. For the interactive methods, we adopt their trained models and evaluate them datasets without further
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Table A2: Meta information of the used datasets. Some public datasets did not disclose device information or lacked the details of contrast phase information.
Dataset
Scanner
Contrast
MSD06-Lung (Antonelli al.
2022
Contrast-cahanced
NSCI.C- Rai.n-nnnngeniiccs Bakr
Contrast enhanced
NSCLC Radiomics Zhang et
2017
Siemens
Contrast enhanced
LIDC (Amato
2011
GE.Philips. Toshiba.Siement
Contrast-chaaseed
NSCLC PleuralF/lusion
Arrieta et al.
2019
Siemens
Contrast enhanced
COVID-19 (Roch al.
2022
Mixed
Non-centrast
(Antonelli
2022
Contrast enhanced
MSD08 Hepatic (Antonelli et al 2022
Contrast enhanced
WAWTACE (Bartnik al. 2024
GE,Siemens, Phillis,Toshibe Toshibu
Non-contrast Arteril,, Portal Velaye
HCC (Morshid et al. 2019
Contrast enhanced
MSDO7-Punereas Antonelli ot 2022
Contrast-enhaneed
PANORAMA (Alves
KITS23 (Hllerr et al. 2025
Toshiba, Siemens, Sieeeee,,,,,eeeeee Philipss
Contrast Contrast-enhanced
Adrenal
Ahmed
2020
Wankesha WI,UAA
Contrast-enbansed
MSD10-Colomt Anttonelli
2022
Contrast-enhancee
Atlos Bassi ul.
2025
Mixed
Multi-contrast phase
Rider
2014
Siemens
Contrast enhanced
Corona Paiva
2020
Non-contrast
IRCADb Soler al
2010
Contrast-enhancer
CHAOS Kavur et
2021
Contrast-enhansee
TCIA- Puncreas (Roth al
2016
Siemens
Contrast-enhanced
Siemens, General Electric, Philips, Toshiba,Barcs, Vitl,
Plain, artery. portal,delay
FLARE (M 2024
Table A3: Comparison methods this work.
Method
Publications
Interactive segmentation
(require manual prompts)
Seg Vol (Du al. 2024)
NeurIPS'24
SAT (hhao et al.
2025
Npj Dig. Med'25
ULS de Grauw el 2025
MedIA'25
LesionLocator (Rokuss et al.
2025
CVPR'22
Pan-cancer segmentation
(automatic screening)
nnUNet (Isensee et al.
2021)
Nature Methods 21
SwinUNETR (Hatamrzadch et al.
2021
MICCAIW'21
3D UX-Net (Lee et al.
2023
ICLR'23
CLIP-driven (Liu et
2023
ICCV'23
TransUNet Chen et al.
2024a)
MedIA'24
UniMiSS+ Xie et
2024
TPAMI'24
VoCo (Wu et al. 2025a
CVPR 24
SuPreM [L. et al.
2024
ICLR'24
PASTA (Le1 et al.
2025
arXiv '25
finetuning. Since external datasets already included in their training, only compare them on the internal datasets. The comparison methods are listed in Table A3
Evaluation. For segmentation, report the Dice Similarity Coefficient (DSC) results. For detec- tion, we simply adopt the mask-based detection approach following previous methods (Cao et al.) 2023 Hu al. 2025 Chen al.) (0024)), i.e., once the segmentation prediction overlaps with the ground truth, this case is assumed as detected. We report the F1-Score for each CT scan follow- ing (Cao al. 2023 Hu al. (025). We further evaluate the false positive rates on three datasets without lesions, i.e., CHAOS (Kavur et al. 202T),
(Roth et al. 2016). and Atlas (Qu
al. 2023).
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Table A4: Pre-processing details and Training settings.
Clipped HU for chest CT
[-900, 650]
Clipped HU for abdomen CT
[-175, 250]
Crop Size
196, 96, 64]
Spacing
[1.0, 1.0, 3.0]
Backbone of the Glance model
3D ResNet18 (Chen et al. 2019)
Backbone of the Focus model
SwinUNETR (Hatamizadeh
2021
Network Parameters
104M (Glance model 32M, Focus model 72M)
Glance model Loss
-IGRL (0) (Eq
Focus model Loss
Dice-CE
Optimizer & Scheduler
AdamW & Cosine
Batch size
16
Learning rate
3e-4
Training epochs
300
6.2 SUPPLEMENTARY RESULTS
Table A5: Pan-cancer segmentation performance (DSC %) 16 datasets across 9 types of lesions. We select the no-lesion cases from Atlas (Bassi al. (025). We report the results of automatic screening models.
The internal datasets are described in Table Al
Method
MSD06
Radiogonomics
Radiomics
LIDC
Pleural.
COVID-19
MSD03
MSD08
anUNet
48.7
61.0
48.4
48.1
36.8
57.7
33.6
SwinUNETR
48.7
71.0
42.6
43.6
26.3
58.5
48.5
47.9
3D UX-Net
45.3
70.8
35.6
30.1
10.2
56.
51.4
45.2
CLIP-driven
50.0
71.7
40.6
36.8
22.8
59.7
$7.0
TransUNet
54,9
69.4
46.5
36.7
17.9
60.8
48.8
57.2
UniMiSS+
53.1
65.8
44.7
40.3
23.6
62.0
$7.0
45.5
54.2
71.8
49.3
49.4
41.6
63.4
59.5
SuPreM
57.9
68.2
48.0
48.0
38.5
64.1
62.5
55.1
PASTA
56,0
64.3
48.7
48.2
23.3
64.7
58.5
59.2
GF-Sereen
62.8
73.6
53.3
$5.2
45,0
69.5
63.4
59.5
WAWTACE
PANORAMA
KFTS23
nnUNet
68.4
72.8
42.3
32.6
68.3
86.6
30.6
92.6
SWinUNETR
67.8
70.5
30..
26.3
70.8
83.11
21.0
84.1
3D UX-Net
63.3
25.4
11.6
62.3
6.9
83.3
CLIP driven
63,9
14.8
67.0
20.2
87.1
TNet
63.7
68.8
34.7
27.4
64.7
87.7
14,5
85.8
UniMiSS+
61.4
68.5
29.9
18.0
64.1
83.6
12.1
90.4
VoCo
75.6
33.2
70.5
SuPreM
75.3
76.2
45.4
37.5
71.2
88.0
21.9
92.4
PASTA
75.1
76.1
29.2
31.6
72.1
88.6
29.3
91.5
GF-Screen
75.6
80.2
51.8
45,9
75.3
37.7
93.2
Table A6: We conduct five experiments for GF-Screen, and report the mean and standard deviation (std) segmentation DSC across 9 lesion types.
Method
Lung
Lung
Pleural
COVID-19
Liver
Pancreas
Kidney
Adreno.
Colon
tumor
nodule
effusion
infection
tumor
carcinoma
Gereen
57.5+0.
55.4
45.0
69.02ar
67.620.
49.220.
75.3
91.5
38.1
Table A7: We conduct five experiments for GF-Sereen, and report the mean and standard deviation (std) of detection FI-Score across 9 lesion types.
Method
Lung
Lung
Pleural
COVID-19
Liver
Pancreas
Kidney
Adreno.
Colon
tumor
nodule
effusion
infection
tumor
tumor
tumor
curcinoma
tumor
GF-Sereen
96.440.2
91.-a1
96.6
100.0
98.2.n.2
15.1
100.0go.
100.0.0o
85.0go.
Table A8: The t-test p- values compared with the best-competing method VoCo (w et al.
2025
P-values
Lung
Lung
Pleural
COVID-19
Liver
Panceess
Kidney
Adreno.
Colon
nodile
eflusion
Infection
Segmentition
3.51
Detection Fl-sovees
1.80 x 2.06
4.11 x
2.54 x 10
1.53 x
9.13
8.10
1.15 x
8.90 x 10
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Table A9: We conduct ablation studies on the sliding window sizes across different types of lesions.
Sliding window size
Lung tumor
Lung nodule
Pleural effusion
COVID-19
Liver tumor
Pancreus turnor
Kidney tumor
Adreno. curcinoma
Colon
infection
tumor
64 x 64 x
54.6
53.8
44.1
68.2
64.5
46.0
71.8
90.2
34.3
96 x 96 64
57.5
55.4
45.0
69.0
67.6
49.2
75.3
91.5
38.1
128 128 64
57.8
55.6
42.4
69.1
67.0
49.5
74.4
91.4
38.9
We present the dataset-wise results on 16 datasets complement T] as shown in Table To evaluate the training deviation of GF-Screen, we conduct five experiments for GF-Screen, and report the mean and standard deviation (std) in Tables A6 and A7 We further summarize the
comparisons with the second-best model (Wu et al. 2025a) in Fig, A1
We also conduct ablation We set the size of the sliding
studies on the sizes of the sliding window, as shown Table A9 window as 96 x 96 x 64 to balance the performance and inference efficiency. We observe that
64 x 64 x 64 is worse, while the results of 96 96 64 and 128 x 128 64 are both competitive.
100
GF GF-Screen
66-
60-
28-
Lung Lung
Average
Figure AI: We further summarize the comparisons with the second-best model (Wu et al. GF-Screen is 5.7 times faster with higher performance simultancously.
2025a
Visusl actiotion
Lasion raak
Visual actlustion
Losion mook
Visuol activaioon
Lasion
Figure A2: We present the visual activation maps of the Glance model compared with the Ground truth lesion masks. In the last row, we show some failure cases. For example, in the first case of the last row, the model misses the tiny lesion in the left upper part.
6.3 VISUALIZATION ANALYSIS OF THE GLANCE MODEL
We analyze the classification results of the Glance model in Fig A2 We present the visual activation maps of the Glance model compared with the ground truth of lesions. It can be seen that the Glance level, providing optimal diagnostic
model can effectively detect the lesions at coarse sub-volume views for the Focus model for segmentation. We show some failure cases in the last row, where the Glance model misses tiny lesions fails to indicate the precise regions.
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6.4 REDUCE FALSE POSITIVES
We observe that previous methods (Wu et al. 2025a: Li et al. 2024 Lei et al. 2025 Hatamizadehi
al. 2021 Liu al. 2023 Xie et al. 2024 Lee al. 2023) generally gain high false positives
healthy regions (Table where our GF-Screen can effectively address this challenge by discarding
the redundant regions during inference, shown in Fig. A3 By reducing 23.1% false positives, GF-Screen can also effectively improve the overall DSC scores.
Figure A3: We present the segmentation results on healthy regions (no lesions), compared with the best-competing method (Wu et 2025a
6.5 EFFECTIVENESS OF SELECTING OPTIMAL DIAGNOSIS VIEWS
GF-Screen improve the segmentation DSC by selecting the optimal diagnosis views, as shown in The optimal view (in red) contains the complete organ and provides important information (e.g.. intensity contrast) for cancer diagnosis, while the challenging view (in blue) contains partial information. Thus, the segmentation predictions of this optimal view would be more accurate. Fol- lowing previous methods, we adopt sliding-winng window inference to crop sub-vou volumes with overlapped areas. However, during sliding- window inference, previous methods generally average these results, where the segmentation predictions from challenging views can degrade the average performance of the final results. Our GF-Screen can effectively select optimal views and discard the suboptimal views, mitigating their negative influence.
Fig.A4 A4
Optimal view for civgnosis
Effective aegmentatiin
Segmentation
Challnging
Prediction
With
Siddng-wwisdw Optimal view for diagnosis
Effective segmentation
Sepmentation
X
Predictions of sub opdimal views discanded
Diecard
Prodiction
Challenging view diagnosis
Figure A4: An illustrative case to demonstrate the effectiveness of selecting optimal views. (a) Without GF-Sereen, segmentation predictions from challenging views degrade the average per- formance of the final results. (b) With GF-Screen, only the segmentation results from optimal diag- nostic views are retained, while predictions from suboptimal views filtered out.
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6.6
SEGMENTATION RESULTS
truth
GF-Soreen
GF-Sareen
Lung turor
Lung nodule
Pleural effusion
COVID-18
Liver tumor
Pancreas tumor
Kidney tumorr
Adrenal carcinoms
Colon tumor
Figure A5: We present the segmentation results of GF-Screen across different types of lesions.
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Figure A6: We randomly split our training dataset into five subsets and perform training to analyze
the loss curves of RL.
6.7 DERIVATION OF THE KL DIVERGENCE APPROXIMATION
In Eq. we utilize a specific form for the Kullback-Leibler (KL) divergence regularization term DKL. which originates from the GRPO algorithm (Shao et al. 2024). This form is an approximation of the standard reverse KL. divergence, derived via second-order Taylor expansion. Here, we provide the detailed derivation for clarity.
The standard reverse KL divergence between two distributions G and Gref for given input ey is defined as:
Datandland (GI(Gre)) = Eor-G(() log G(o,loz) Gres(os(u:)
Let us define the ratio r(o.) =
Gres(o:(os) We can re-express the log term as: G(oi)))
log G(o))))) logr(o:).
(8)
The standard KL divergence then becomes:
Datandard (G][Gref) EoinG |- |- logr(o:))
(9)
Now, consider the function f(r) log(r). We perform second-order Taylor expansion of f(r)
around r = 1 (which corresponds to the point where G and Gref are identical).
= x(1) (r) - 1)
2 (1)(
- 1)2
(10)
- log((1 + (- (r - 1) + 2
1
(r - 1)²
(11)
12
(r 1) (r 1)2
(12)
2
1
= + (r²
2 2r 1)
(13)
3
2 p2 - 2r +
(14)
2
Its second-order Taylor expansion around is:
g(r) ~ (1) + +1))))((()))(---- 1) + /0 (1)(r-1}))))) 112
(15)
1 - : (r (r- 1) + 2/
/ 1
(1 - log 1 - 1) +
(r - 1)2
(16)
13
=0+ (r- - 1) こタ (r 1)2
(17)
2 (r- 1)2.
1
(18)
Notice that g(r) = - logr is always non negateve and has minimum of 0 at r = 1, sharing key properties with the KI. divergence. More importantly, its expectation (r((o))) recovers
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the form used in objective:
Eo (o) (Ger(orles)
)
Gres(oslui) log Gres(o.(us) G(o: (16)
Box~G
(19)
G(orloe)
G(oilu)) (as defined in Eq. 4
= DxL (Gk(GGr)))))
(20)
This form, -- logr - 1, is preferred in policy optimization algorithms like GRPO because it serves principled, computationally friendly surrogate loss. It penalizes large deviations of the policy G from the reference Gref (i.e., when deviates from 1), helping to ensure stable training, while being easier to optimize in practice than the exact KL divergence.
6.8
THE USE LARGE LANGUAGE MODELS (LLMs)
We use LLMs (DeepSeek) only to check whether there are grammar errors.
6.9 REPRODUCTIBILITY STATEMENT
We promise that our code and model checkpoints will be released. We have submitted our Docker image with our code and model checkpoints for public leaderboard validation, ensuring results are trustworthy and easy to adopt.
6.10 FUTURE DIRECTIONS
Our main contribution lies in developing the first RL framework for tackling the challenges pan- cancer screening, while the adaptation of GRPO only constitutes the optimization part of the whole framework. Our framework can potentially integrate more emerging policy optimization algorithms beyond GRPO in the future. We will further involve more datasets and lesion types in training and validation, e.g., including head-neck CT datasets for lymph node detection. And we will explore combining the classification of different lesion types into framework. In the future, will further explore the usage of GRL to advance more medical image analysis tasks.
Although GF-Screen has achieved superior performance 23 public datasets, further exploration of its application to real-world datasets necessary to substantiate its effectiveness in clinical practice. In the future, will also include clinician-in-the -ntteian---tthe-loop evaluation and human-Al comparison to strengthen the evaluation of our method. We will work with our collaborating hospitals, e.g., conduct reader study using real-world data. We will compare the diagnostic efficiency and accuracy of radiologists with and without Al assistance from GF-Screen.
6.11 CONCLUSION
We introduce GF-Sereen, a Glance and Focus RL framework for tackling the challenges in pan- cancer screening. GF-Screen employs a Glance model to select diseased regions and discard redun- dant healthy regions, where the diseased regions are fed into the Focus model for precise segmen- tation. We innovatively leverage the segmentation results of the Focus model reward the Glance model via RI,, effectively encouraging the Glance model to prioritize high-advantage predictions. Extensive experiments 16 internal and 7 external datasets across lesion types demonstrated the superior performance of GF-Screen. In summary, our work did not aim to propose a new theory for RL. Instead, our contribution is introducing the first RL framework specifically designed for pan-cancer screening, which is novel and practical conceptual breakthrough in this field.
24


Reviews:
[{"review_id": "T4xf0qpaAU", "reviewer": "Reviewer_HUYR", "summary": "This paper presents GF-Screen, a novel framework for pan-cancer screening in large-scale CT volumes. The core problem addressed is the significant challenge of locating small, diverse lesions within vast, mostly healthy, anatomical regions—an issue of extreme foreground-background imbalance. The proposed solution is inspired by the \"glance and focus\" strategy of radiologists. It consists of two main components: a Glance model that acts as a selector to identify promising sub-volumes, and a Focus model that performs detailed segmentation only on the selected regions. The key technical contribution is the method for training the Glance model. Since the selection action is non-differentiable, the authors employ a RL approach where the Focus model's segmentation output provides a reward signal to the Glance model. A novel GRL paradigm is introduced to optimize this policy. The method is validated on a large, diverse dataset aggregated from 23 public sources and shows outstanding results, notably leading the MICCAI FLARE25 challenge validation leaderboard by a significant margin and demonstrating a 5.7x reduction in computation.", "questions": "See weakness 1.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The results are the most significant strength. Leading the FLARE25 challenge validation leaderboard and outperforming the FLARE24 champion solution by such a large margin (+25.6% DSC) is a truly remarkable achievement.\n2. The method directly addresses a critical bottleneck in deploying AI screening models: computational cost and false positives. By intelligently discarding healthy regions, the 5.7x computational saving is a major practical breakthrough, making large-scale screening far more feasible.\n3. The use of RL to couple a detection/localization network (Glance) with a segmentation network (Focus) is elegant. The segmentation result (a binary overlap reward ) is used as a rich, task-specific feedback signal for the localization model, which is more sophisticated than training the localizer on simple binary \"lesion present/absent\" labels. The proposed Group Relative Learning (GRL) appears effective, as shown in the ablations.", "weaknesses": "1. The paper introduces GRL as a \"novel\" paradigm, but its precise mechanism and distinction from existing policy gradient methods (like PPO, which is cited) could be clearer. The loss function in Eq. 3 looks like a PPO-style clipped objective. The novelty seems to be in the advantage estimation or the \"group relative comparison\", but this specific aspect is not explained in sufficient detail. This lack of clarity is particularly problematic concerning the core RL mechanism. For instance, the paper needs to explicitly detail the action space. The Glance model's role is to select (1) or discard (0) a sub-volume, which is a discrete, non-differentiable step. The paper should clarify how this binary decision is derived from the policy network. Is the model's output 'o' (as referenced around line 206) a probability (e.g., a sigmoid output) from which an action is sampled? If 'o' is treated as a discrete 0/1 output directly, it's unclear how the policy gradient can be backpropagated for optimization. A more detailed explanation is required of what exactly constitutes the 'action' in the RL framework (e.g., the binary selection), how this action is stochastically sampled from the policy during training to enable exploration, and how the underlying policy (the probability distribution over actions) is updated via the GRL objective.\n2. The primary justification for the RL approach is the failure of a standard classification (CE) baseline . However, the paper does not specify how this baseline was trained. Given the stated problem of \"severe foreground-background imbalance\", a simple CE loss is expected to fail. A fair comparison would require comparing GRL to a strong classification baseline", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nv3Z24Sn2x", "reviewer": "Reviewer_xJb3", "summary": "This paper proposes GF-Screen, a novel reinforcement learning (RL) framework designed for pan-cancer screening from large-scale CT scans. The method mimics the radiologist’s diagnostic process — “glance” to locate suspicious regions and “focus” to perform detailed lesion segmentation. GF-Screen integrates a Glance model for sub-volume selection and a Focus model for segmentation. The Glance model is optimised through a Group Relative Learning (GRL) paradigm, where rewards are derived from segmentation outcomes of the Focus model. Experiments on 5,117 CT scans from 23 public datasets, covering nine lesion types, show substantial improvements over previous approaches, achieving the top rank on the MICCAI FLARE25 challenge, with reported +25.6% DSC and +28.2% NSD compared to the FLARE24 champion solution. The paper emphasises efficiency gains (5.7× faster inference) and reduced false positives through selective attention to diseased regions.", "questions": "plz see my detaild comments above", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Strengths\n1. The “glance and focus” paradigm is a clever conceptual and algorithmic adaptation of clinical diagnostic reasoning to AI-based screening.\n2. The paper provides a well-defined pipeline linking reinforcement learning and segmentation, with precise mathematical formulations for the GRL optimisation objective.\n3. Addressing pan-cancer screening rather than single-organ segmentation is highly ambitious and clinically meaningful.\n4. The study presents extensive experiments on both internal and external datasets, including strong validation on the FLARE25 leaderboard.\n5. The demonstrated 5.7× reduction in computational cost without accuracy loss is a notable contribution for real-world clinical deployment.\n6. The quantitative results convincingly show that GF-Screen outperforms a range of established baselines, including nnUNet, SwinUNETR, VoCo, and PASTA.\n7. The adaptation of GRL to vision-based medical tasks is novel and extends recent RL developments in a meaningful way.", "weaknesses": "Weaknesses\n1. While the proposed framework is well-engineered, its novelty may primarily lie in combining existing ideas (sub-volume selection, segmentation, RL reward optimisation) rather than introducing a fundamentally new theoretical concept.\n2. The reinforcement learning formulation, especially the reward design and advantage estimation, is largely empirical. There is little discussion of convergence, stability, or theoretical motivation for using group relative learning in medical imaging.\n3. The paper would benefit from more detailed ablation studies isolating the effects of the RL component, the GRL mechanism, and the segmentation backbone. Interpretability analyses (e.g., qualitative localisation or attention visualisations) are limited.\n4. While the authors state that code will be released, reproducibility remains uncertain. Essential hyperparameters (e.g., coefficients α, β, learning rates, or update frequency of G_ref) should be clearly stated in the main paper rather than deferred to the appendix.\n5. The dataset curation process aggregates 23 datasets with differing acquisition protocols and lesion distributions. Without normalisation or harmonisation details, there is a risk that model performance may rely on dataset-specific biases.\n6. The training process of the Glance model may be unstable due to non-differentiable sub-volume selection, but the paper does not report training curves or sensitivity analyses beyond one brief figure.\n7. Despite strong quantitative results, the absence of clinician-in-the-loop evaluation or human–AI comparison weakens the practical validation of the proposed clinical analogy.\n8. The paper is technically dense, with long paragraphs and numerous references, which may hinder readability for non-specialist reviewers. Some restructuring and pruning would improve clarity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SnKPBXsyJy", "reviewer": "Reviewer_bZ4d", "summary": "This work presents GF-Screen, a Glance-and-Focus reinforcement learning framework for pan-cancer screening in CT scans, inspired by radiologists’ diagnostic workflow. A Glance model identifies suspicious sub-volumes, and a Focus model segments lesions, with segmentation results rewarding the Glance model via a group relative learning strategy. Trained on 5,117 CT scans and evaluated across 23 datasets (9 lesion types), GF-Screen leads the MICCAI FLARE25 leaderboard, surpassing the prior champion by +25.6% DSC and +28.2% NSD, while reducing computation by 5.7× through efficient region selection. This marks a practical step in accurate, scalable, and efficient AI-based pan-cancer screening.", "questions": "1. The term \"data curation\" is used, but simply aggregating public datasets does not constitute curation. Could the authors clarify their curation process? Specifically, how were label inconsistencies or annotation discrepancies across source datasets identified and resolved?\n2. Regarding the FLARE25 challenge results: the reported margin over the second-place method is substantial. Was this comparison conducted under equal conditions? In particular, did all participants use the same training data? Does the challenge permit the use of external data?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The authors effectively reframe the well-established coarse-to-fine paradigm as a \"glance-and-focus\" strategy, an evocative and clinically intuitive framing that demonstrates strong scientific storytelling.\n2. The figures are clean, well-designed, and complemented by clear, concise textual descriptions, making the methodology easy to follow.\n3. The empirical evaluation is extensive, with experiments across multiple internal and external datasets and diverse lesion types, providing thorough support for the paper’s claims.", "weaknesses": "1. The motivation for using reinforcement learning (RL) to train the Glance model is underdeveloped. While extreme class imbalance is acknowledged as a key challenge, the authors do not sufficiently justify why conventional approaches, such as intelligent sampling, focal loss, or hard-negative mining, would be inadequate. Jumping directly to RL, a currently popular but complex paradigm, feels more like a methodological trend than a necessity.\n2. Within the RL framework, using segmentation performance from the Focus model as the reward signal for the Glance model introduces undesirable coupling between the two stages. Specifically, a high segmentation score may simply reflect an \"easy\" sub-volume (e.g., one with clear boundaries or canonical orientation), not necessarily one that was optimally cropped. Conversely, a low segmentation score might indicate a challenging but clinically critical case (e.g., partial lesions or suboptimal viewing angles), precisely the cases the Glance model should prioritize. The current reward scheme risks biasing the Glance model toward easy regions and discarding hard, ambiguous, yet important ones.\n3. The paper’s layout could be better optimized for impact. Given space constraints, Table 1 (dataset statistics) could be moved to the supplementary material to free up room for more critical content, particularly ablation studies and a dedicated conclusion section, which is notably absent and atypical for a conference submission.\n4. From a methodological standpoint, the work primarily constitutes an application of the GRPO algorithm to a new domain, rather than a significant algorithmic advance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UMfGohkEZn", "reviewer": "Reviewer_Cyrm", "summary": "This paper presents a novel end-to-end reinforcement learning framework for cancer screening. The proposed method consists of a sliding window-based approach (glance) to first identify CT sub volumes and a focus model to segment the lesions. This glance-and-focus approach demonstrated superior performance on multiple internal and public datasets, as well as the Miccai FLARE25 challenge.", "questions": "1. How is the size of sliding window decided? Different tumors, for example, liver tumor and lung tumor, differentiate in size. For the same type of tumor, it could also have inter-patient variations.\n\n2. Did the author perform any statistical analysis of the results in Tables 2 and 3?", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The idea of applying group relative learning for sub-volume selection is innovative. Moreover, the reward design that incorporates segmentation results as reinforcement feedback is well-motivated. This strategy effectively distinguishes sub-volumes with partial lesion overlap, enabling more accurate and meaningful reward signals for reinforcement learning.\n\n2. The proposed method is fast in inference by discarding redundant regions, which reduces computational cost.\n\n3. The experimental evaluation is comprehensive, including comparisons with multiple strong baselines and demonstrating consistent superior performance across all tasks.", "weaknesses": "1. Equation 4 has some jump in thoughts. I understand that it comes from the second order Taylor expansion of KL divergence and serves as a surrogate of KL divergence. However, it is not straightforward to readers. I recommend explaining this jump in the supplementary materials.\n\n2.The evaluation datasets only contain abdominal CT images. It is unclear how the model performs on other parts of the body, e.g., head CT. \n\n3. It would be beneficial to the paper to present the protocols and device of the CT images in use. This could benefit the robustness of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)