You Point, I Learn: Online Adaptation of Interactive Segmentation Models for Handling Distribution Shifts in Medical Imaging
Authors: Wentian Xu
Ziyun Liang
Harry Anthony
Yasin Ibrahim
Felix Cohen
Guang Yang
Konstantinos Kamnitsas
Venue: ICLR 2026 Poster (2026)
Keywords: Medical Image Segmentation
Interactive
Online Adaptation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts, this paradigm naturally lends itself to such challenges. In medical imaging, where distribution shifts are common, interactive methods can use user inputs to guide models towards improved predictions.
Moreover, once a model is deployed, user corrections can be used to adapt the network parameters to the new data distribution, mitigating distribution shift. Based on these insights, we aim to develop a practical, effective method for improving the adaptive capabilities of interactive segmentation models to new data distributions in medical imaging.  Firstly, we found that strengthening the model's responsiveness to clicks is important for the initial training process. Moreover, we show that by treating the post-interaction user-refined model output as pseudo-ground-truth, we can design a lean, practical online adaptation method that enables a model to learn effectively across sequential test images. The framework includes two components: (i) a Post-Interaction adaptation process, updating the model after the user has completed interactive refinement of an image, and (ii) a Mid-Interaction adaptation process, updating incrementally after each %user 
click. Both processes include a Click-Centered Gaussian loss that strengthens the model's reaction to clicks and enhances focus on user-guided, clinically relevant regions.
Experiments on 5 fundus and 4 brain‑MRI databases show that our approach consistently outperforms existing methods under diverse distribution shifts, including unseen imaging modalities and pathologies.
Code: https://github.com/WenTXuL/OAIMS

Published as a conference paper at ICLR 2026
YOU POINT, I LEARN: ONLINE ADAPTATION OF IN- TERACTIVE SEGMENTATION MODELS FOR HANDLING DISTRIBUTION SHIFTS IN MEDICAL IMAGING
Wentian Xu, Ziyun Liang, Harry Anthony, Yasin Ibrahim, Felix Cohen, Guang Yang, Konstantinos Kamnitsas Department of Engineering Science University of Oxford
ABSTRACT
Interactive segmentation uses real-time user inputs, such mouse clicks, to iter- atively refine model predictions. Although not originally designed to address dis- tribution shifts, this paradigm naturally lends itself to such challenges. In medical imaging, where distribution shifts are common, interactive methods can use user inputs to guide models towards improved predictions. Moreover, deployed, user corrections can be used to adapt the network parameters to the new data distribution, mitigating distribution shift. Based on these insights, we aim to develop practical, effective method for improving the adaptive capabilities of interactive segmentation models new data distributions in medical imaging. Firstly, we found that strengthening the model's responsiveness to clicks is im- portant for the initial training process. Moreover, we show that by treating the post-interaction user-refined model output pseudo-ground-rrung a lean, practical online adaptation method that enables a model to learn effectively across sequential test images. The framework includes two components: (i) Post-Interaction adaptation process, updating the model after the user has com- pleted interactive refinement of an image, and (ii) Mid-Interaction adaptation process, updating incrementally after each click. Both processes include a Click- Centered Gaussian loss that strengthens the model's reaction clicks and en- hances focus on user-guided, clinically relevant regions. Experiments on fundus and 4 brain-MRI databases show that our approach consistently outperforms exist- ing methods under diverse distribution shifts, including unseen imaging modalities and pathologies. Code: https://githubb.cmm wtt.ttuww....eee..eeeeee wwue/WuTTTu//AMM
model is
we can design
INTRODUCTION
Medical image segmentation facilitates discase analysis, diagnosis, and treatment. Deep-learning methods have driven notable advances in automated medical image segmentation (Azad et al. 2024). However, a major challenge is that the training-data distribution often differs from the test-data dis- tribution-for example, images may be acquired on different scanners-sewe severely hindering model performance, Although models lack knowledge about unseen test data distributions, human users (such as clinicians) are often still able to segment images in the target distribution with reason- able accuracy. Hence, their knowledge can be leveraged to guide models. Can we design an AI framework that enables models be guided by human users an easy, immediate, and continuous manner, so that they can effectively adapt to distribution shifts? Although not originally developed for solving data distribution shift problems, class of deep learning models known as interactive segmentation models is well suited this challenge.
Interactive segmentation models allow users to provide prompts, such as clicks, scribbles, or bound- ing boxes, which inform the model's prediction. A common strategy is to encode user prompts as additional input channels in convolutional networks, as seen in models like DeepIGeoS (Wang et al,) 2018) and Interactive FCNN (Sakinis et al. 2019). More recent approaches, such SAM (Kirillov ct al. 2023). MedSAM (Ma et al. 2024), and Med-SA (Wu et al. (023b), instead employ Trans- formers encode user prompts. Both approaches have demonstrated strong performance on natural

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Published as a conference paper at ICLR 2026
Inference Overview
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Figure I: Method overview. For Pretraining, the model is trained with simulated clicks, provided as additional input channels besides the image. During Inference and adaptation, images arrive sequentially. For each image, the user iteratively provides T clicks to correct the segmentation, until the final prediction Phnnl = Pr is obtained. Mid-interaction adaptation: After each corrective click Ct, the model's s output Pinitial is used pseudo-label compared with the pre-correction output P.-1 via the DF and CCG losses, to update model parameters. The updated model then produces refined output P.. which is then shown to the user, ending iteration t. Post-interaction adaptation: Once the final corrected segmentation Pr is obtained, it is used as pseudo-label to first fine-tune the model using a localization click (Stage 1), and then fine-tune using multiple correction clicks, generated from areas where the prediction of Stage disagrees with Pr (Stage 2).
and medical images, highlighting the usefulness of incorporating user guidance. However, they do not include mechanisms for adapting model parameters from user corrections.
Our work explores how to leverage user guidance in interactive segmentation to improve perfor- mance under distribution shift most effectively. We identify that this requires complementary learning mechanisms for the pre-deployment training and post-deployment adaptation. For pre- deployment stage, we find that adding an optimization objective that enforces model predictions to align with user feedback in areas around given clicks improves performance under distribution shift.
We couple this with post-deployment learning mechanisms. Post-deployment adaptation methods for interactive segmentation optimize a model for the specific data distribution encountered after deployment using information from user prompts. They Online Learning (Hoi et al., (021) to update model parameters after each test image is processed sequentially D Prior work on online adaptation for interactive medical image segmentation is limited. An early related method is IA+SA (Kontogianni et al., 2020), originally developed for natural images, which combines independent image-level adaptation (TA) and image-sequence adaptation (SA). Another recent related method, TSCA (Atanyan et al., 2024), achieves further improvements in performance after online adaptation. These methods leverage user corrections through sparse cross- roos--ntrropy or focal loss, applied only to the clicked pixels. This focuses optimization narrowly on a small number of pixels while ignoring surrounding areas. Moreover, additional regularizers are often required to prevent overfitting to the few labeled pixels, increasing model complexity and the number of hyper-parameters.
Our work is based on the insight that, a real-world interactive segmentation workflow where the user provides clicks to correct model output, the segmentation predicted at the end of interac- tions should have sufficient quality to serve as pscudo ground-truth. We propose a Post-Interaction adaptation method based on using that final prediction optimization target and show it results in effective adaptation without requiring complex regularizers. Furthermore, we generate artificial correction clicks from the srruud--rrond--ee -rrth mask and use them with the Click-Centered Gaus- sian (CCG) loss that we introduce to strengthen the model's 's response around clicks under the new data distribution. This improves performance in all tested distribution-shift scenarios.
Adaptation in this context targets only the distribution seen post-deployment, thus does not need to handle Catastrophic Forgetting of past distributions Continual Learning (Wang et al. 2024
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We further combine this with Mid-Interaction Adaptation, which adapts the model weights after each user click. We again rely on the segmentation mask predicted by the model and optimize the CCG loss, which emphasizes the region around the click. Unlike previous works that focus only on the clicked pixels for such adaptation, our method leverages the whole corrected segmentation mask together with the CCG loss, thereby optimizing over the greater surrounding region, improving adaptation performance.
We term the proposed method OAIMS (Online Adaptation for Interactive Medical-image Segmen- tation). Experiments under distribution shift across 5 fundus and 4 brain-MRI databases demonstrate that by using solely the proposed Post-Interaction method already results in adaptation performance that compares favorably to SOTA adaptation methods. When this is combined with Mid-Interaction adaptation, OAIMS consistently outperforms all previous methods, especially on brain MRI where Dice score improvements exceed 10%. Ablation studies show that the proposed CCG loss is con- sistently useful when employed in all 3 learning processes (pretraining. mid- and post- adaptation). Further analysis also shows strong robustness to settings that may cause overfitting other methods.
2
METHODS
2.1
OVERVIEW: INTERACTIVE SEGMENTATION FRAMEWORK
We here provide an overview of the whole process, shown in Fig. I with the detailed algorithm of the process provided in the Appendix AA.6] For simplicity, we describe it for binary segmentation, but it also applies for multiple classes, as shown in Experiments.
We define the interactive model as J(T, CT,C;)), 0), where is the input image, C is the set of user clicks, and are model parameters. click is labeled either as foreground or background class. A foreground click indicates that the specific pixel belongs to the target object, background click indicates that it does not. We train the model on source database with simulated clicks C. During inference, the model receives a sequence of images (I1. I2...., INY } from another database. For single image In separately, the user (or simulated user) first provides a localization click e₁ to trigger the interactive process. The localization click used to start interaction is simply foreground click placed anywhere inside the target foreground object. The model predicts initial segmentation PT [In, c1; 8). Afterwards, multiple iterations of interactions occur. At iteration t the user places new click e, in a region where prediction P._ is wrong. The click set is updated C, = C,-1 J } where C1 {ci]. )- The model then predicts P. = f[nn, 0); Next interaction t. + then occurs, and so forth. After T interactions we obtain the final prediction PT. which we call P final- While here T's is given a set value for simplicity, in a real-world setting T would be much as user requires to be satisfied with segmentation output. The whole process is then repeated for the next image Ln+1 in the sequence. For notational simplicity, we omit the image index n in most formulas below.
During inference, we perform two types of online adaptation. The Post Inst-lnteeaction adaptation is two-stage method that updates the model after the iterative, interactive corrections for a single image have finished and the model has produced final segmentation Pinal- This improves performance for subsequent images. Mid-Interaction adaptation happens after each interaction. takes place before the Prinal is obtained. This strategy benefits both the current and subsequent images.
2.2 PRETRAINING THE INTERACTIVE MODEL
The base interactive model is a U-Net (Ronneberger et al. 2015) modified to accept both the image and click prompts as input. We use the same strategy as ICNN (Sakinis al., 2019), where we set 2 guidance maps that encode foreground and background clicks, respectively, each with the same spatial dimensions as T. The raw guidance maps are zero everywhere except at clicked pixels; we then apply Gaussian smoothing kernel and normalize each map to [0. 1]. These maps are concate- nated with the image along the channel dimension. The concatenated tensor (image + foreground map + background map) is input to the model. We train the base model using simulated clicks and a compound loss: Dice-Focal (Eq. equation 4 and CCG Loss (Eq. equation 3). The CCG Loss proposed herein strengthens the model's response user clicks. Combining Dice with Focal loss is beneficial in medical segmentation to handle the imbalanced number of background / foreground pixels. See Appendix A.2 for details regarding click simulation. We note that the backbone model,
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here Unet for its proven performance and computational efficiency Isensee et al. (2024). could be replaced with other architectures, since our online adaptation method is model-agnostic.
2.3 CLICK-CENTERED GAUSSIAN (CCG) LOSS
An interactive model should react to user clicks and update the surrounding region accordingly. We propose a
Gaussian Loss to strengthen the model's reaction to clicks by penalizing wrong predictions near each click, weighted by Gaussian kernel. This loss employed in all three stages, pre-training, Post-Interaction adaptation, and Mid- Interattraatoon adaptation.
Let denote user click at pixel (f', with class label ye j' E {0, 13. For any pixel (i,j) we define the Gaussian weight and an indicator
exp
(11-))))))) 20
1 81 S 30 and Is - J'1 S 30
G.(r,5)
(I)
otherwise,
P((.)) Wea
1o(t,5) =
(2)
0,
otherwise.
P denotes the ground-truth mask (used for pretraining) or pseudo ground-truth mask (used for adaptation), and j(i.j) is its pixel value at coordinates (i,j).
Given the current prediction P. the CCG Loss is
LocG Ecec ER'' E/= L...) CE(P(1,5) P(.J))
(3)
IC
where C' is the set of clicks for the current sample, H W is the image size, and CE(-,-) denotes cross-entropy loss. The penalty is applied only to pixels that should share the same class as the click using the indicator Jc( For instance, given a foreground click, the loss only applies to surrounding pixels that are foreground in the ground truth mask.
Why not apply the loss to all surrounding pixels? Each click only serves to change the surround- ing pixels to specific target class. The click does not provide information for clusters of pixels belonging different class. Applying extra penalties to pixels annotated as another class in the ground truth may cause the model to overfit to specific regions or images, ultimately degrading overall performance when facing distribution shifts or performing online learning.
2.4 ONLINE ADAPTATION
We now deseribe the online adaptation process, which includes Post-Interaction and Mid- Interrattnnt adaptation phases. All model parameters are updated in all phases.
Post-Interaction Adaptation: This process updates the model after a user has completed correcting the segmentation of an image using a set CT of T clicks, resulting to final segmentation mask P Pinaa Pr = [(I, Cr, 0). The key assumption is that Pinsll after the user finished their corrections is of "good enough" quality to serve as pseudo ground-truth mask for updating the model. In real- world practice this rather easy ensure, if we only adapt based on segmentations for which the user confirmed that the interactions led to satisfying output. Even if Panal is imperfect, it still provides new information from users to update the model's knowledge.
The user interaction starts with a localization click. We therefore naturally split the post-interaction updates into: (i) Fine-tune with an initial localization click as input; (ii) Fine-tune with correction clicks as input.
Stage 1 Fine-tune with Localization Click:
To enhance the model's ability to make a good initial segmentation on the new data, we first fine-tune it with one localization click e₁ as input,
given by the user the previous inference step. Given C1, we obtain P1 [[I, 61 0). We then
update the model by applying Dice-Focal (DF) loss (Milletari al. 2016
Lin al. 2017 to
penalize deviations of P from the user-corrected mask Pinal, where
LDF (1 o) CD + tof..
(4)
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CD. Le and a are the Dice loss, Focal loss, and weighting hyper-parameter respectively. Only one Gradient Descent update is performed for each image using Eq. 4
Stage - Fine-tune with Multiple Correction Clicks: To improve the model's ability to leverage correction-clicks, we input a set of artificial correction-clicks C, obtain model output P- C, and update the model using Pinal target. is not appropriate to reuse the user's original correction clicks Cr, as they were already used to produce Pinal = f(, Cr, 0) -they would result in P being identical to Pinml and hence trivial updates. Instead, we generate a set Ć of clicks by comparing the Stage I output P1 with Pinal, locate false-positive and false-negative regions, and generate one artificial click in each erroncous connected component (up to T clicks), without extra human input. These newly generated clicks Ò are fed to the model, yielding new prediction P. We then apply the proposed CCG loss, along with Dice-Focal loss penalize deviations of P from P Pinal for further guidance. The total loss is:
Lusa CDF B Leco.
(5)
The CCG loss ensures the model reacts to each click in its surrounding region during adaptation, which to our knowledge, no previous work addresses explicitly.
Indd-Interaction Adaptation: Besides lnst-Interattion adaptation, we can also update the model after each user click. In this case, the updated parameters obtained after each click determine the next prediction, so the update not only improves segmentation of the following images but also of the current image. This in turn helps achieve a high quality Prinal after all T interactions, which facilitates effective Interlnnteactton adaptation afterwards, thus complementing and enhancing it.
We again leverage the idea of using pseudo ground truth the model's output after correction by user click. Let Pt-1 = f(1, C,_1, 0z-1) be the prediction after t- 1 clicks. When the next corrective click Ct is given, we obtain pimitial f(1, Ce, 0c-1). 1). We then optimize CCG plus Dice-Focal loss (Eq. 5) to penalize differences between P:-1 and Pinitial using Pt-1 as the prediction and pinitial as pseudo ground truth. This formulation leverages the additional information induced by o to the new state (1) in comparison to the previous state (1 - 1), to optimize model parameters. Here, after each user click, the loss Leea is applied to the latest click o. After the model parameters are updated to 9r. the model processes Ct again and produces P. f(1, C2 0t). P, is shown to the user (or simulator) to get the next click and serves as prediction for the next update. The process is repeated for each of the 7 user clicks, until final prediction Paual Pr is obtained. Finally, Post-Interaction adaptation on that image is applied.
The pseudo ground truth Pinital in Mid-Inntraction adaptation is not perfect, the CCG loss is very important. It helps the model to concentrate learning on regions close to the clicks, which are the most valuable and trustworthy areas. The CCG loss is not intended strengthen the model's reaction here, because the e, is not used for obtaining Pt-1-
3 EXPERIMENTS
Databases: We evaluate our method on two types of data. Fundus imaging: We use 5 public databases: REFUGE2 (Orlando et al. 2020; Fang et al.) 2022). G1020 (Bajwa et al. 2020). GSI- 1- Drishti (Sivaswamy et al., 2014), GAMMA (u et al. (203a),, and PAPILA (Kovalyk et al. 2022 They are 2D RGB images acquired at different clinics with different scanners, hence each represents a different distribution. We perform multi-class segmentation {0: Background, 1: Outer-ring, 2: Cup}. We compute evaluation metrics Cup and Disc, where Disc is the union of Outer-ring and Cup, as common in literature (Orlando et al. 2020). Unless stated otherwise, we treat REFUGE2 as the source database on which we pretrain the interactive model. We treat other databases as different target distributions for adaptation and evaluation. MRIS of Brain Lesions: We use 4 databases. Each contains different type of pathology and some contain multiple MRI modalities: BRATS2023- - Glioma, with Flair, T1, T1c, T2 modalities (Baid et al.) 2021]; ATLAS v2.0 - Stroke, with Tl modality (Liew et al. 2022): WMH - white matter hyperintensities, with Flair and TI (Kuijf) et al. 2022): TBI Traumatic Brain Injuries, with Flair and T1. TBI is the only non-public data we use. Each database is acquired from a different clinic, with different scanner. Each database and different data distribution. Although the MRI scans 3D images, we test the models using 2D slices, by selecting the slice with the largest lesion area per case. For multi-class databases (BRATS, TBI), we merge all lesion classes into one label, and
modality can therefore be regarded
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G1020 (feef
PAPILA (tast sabs)
GS1 (lest
REFUGE joxtt sots) Cup
5200
0.7
0.00
28
0.40
BRATS T1 (teat seta]
BRATS c (leed mats)
0.75
BRATS 12 [teat uubs)
1.000
BRATS Faer (west sebs)
0.77
0.003
0003
0.7
0.000
#1icks
10
Figure 2: Dice-score performance for automatic and interactive models. All networks, except the in-distribution baseline, are trained on REFUGE (fundus) or BRATS-FLA (brain MRI). The x- axis represents the number of clicks. Horizontal lines mark automatic performance of automatic models in cross-distribution and in-distribution settings. Curves show the interactive segmentation model with and without the CCG loss; clicks significantly improve performance in all test cases, with the CCG loss providing additional gains, especially for large distribution gaps (e.g.. BRATS-T1) )
perform binary classification (healthy tissue VS lesion). We split BRATS into 1002 training cases and 249 test cases. Unless stated otherwise, we pre-train on the train split of BRATS using the Flair modality. We then use the other modalities of BRATS's test split, and all other databases, as the target distributions for adaptation and evaluation. All interactive models are trained with up to 10 simulated clicks per image, unless stated otherwise.
3.1 INTERACTIVE SEGMENTATION UNDER DISTRIBUTION SHIFT
We start with experiments that test the performance of the base interactive model under data-distribution shift, without any adaptation. We also assess if the proposed CCG loss helps an interactive segmenter to handle distribution shifts. We consider 2 types of distribution shifts. The first is across fundus databases. We consider REFUGE as the source distribution, and other fundus databases as target distributions. The second is across BRATS modalities. We consider BRATS FLAIR as the source distribution, and other BRATS modalities target distributions.
Four models are compared for each of the settings: (1) Automatic (non-interactive) U-Net (same backbone as the interactive model) trained on the source database, and applied to each of the target databases dcross-distribution);; (2) Automatic model trained on the target database and applied to the target database (in-distribution). This is quantify performance on target, without influence of distribution shift; (3) Interactive segmentation model trained on the source database without CCG loss, applied to each target database; (4) Similar to (3) but pretraining also uses CCG loss. Results are shown in 2 We see a large difference between in-distribution and cross-distribution per- formance of automatic methods, across all settings, due to distribution shifts between source and target data. Nonetheless, ten clicks with the interactive segmentation model largely close the gap, confirming that interactive segmentation remains effective under strong shifts. Adding the CCG loss during pretraining yields improvements in most settings. This is because the CCG loss enforces the model to depend more on user input when given, signal unrelated distribution shift, and less on the image signal where the shift manifests.
3.2 ONLINE ADAPTATION
We then evaluate our online adaptation methods, including Mid-Interaction and Post-Interaction adaptation. For all following experiments, during online adaptation, a sequence of images is input to the model. Each image is corrected through T iterations (one click per iteration, T = 10 by default). For every image, the Dice score is calculated with the prediction mask at each iteration t after mid-in inddittteaation adaptation is performed in that iteration. We measure average Dice score achieved for each image in the image sequence after 1, 5, and 10 interactions. The adaptation methods update the model after getting the segmentation result after each click and each image.

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Performance on fundus imaging (average Dice, %). Each image receives 10 clicks; the base interactive model is trained on REFUGE. Each cell reports Disc/Cup Dice at 1, 5, and 10 clicks. While ICNN' and Med-SA are non-adapting models, the other methods perform online adaptation. PI is our Post- Internntraction adaptation strategy. PI+MI combines the Post- Interanteaatioon with the Mid- Interaction strategy.
Table 1:
G1020
PAPILA
GSI
GAMMA 5
No. C1
5
10
10
3
10
ICNN*
89,4/77.4
93.1/83.1
95.1/88.0
88,
92.6770.2
94.6/77.1
96.682.44
97.2/90.4
97.7/94.1
94,4/833
96.
97.2/93.3
Med-SA
72.2/56.1
75.3/58.6
75.6/58.6
52.5/34.5
56.1/36.7
56.7/36.7
88.6/61.5
89.6/67.0
89.6/67.6
89.8/76.2
90.077.55
90.0/77.5
TA+SA
90.5777.8
148.5 96.1990.
89,4/61.3
93,37003
95.777.6
96.883.4
97.4/91.3 98.1994
94,778400
99.2
97.4991.
TSCA
89.9/77.6
94.2/85.0 96.1/90.7
89.6/61.7
94.072.2 96.1/79.0
96.9/85.4
97.5/92.9
98.0995.5
94.6/84.2
96.4/90.7
97.5/94.1
PL
93.5/81,9 95.9/88.5 97.092.2
94.1/73.0
96.1/80.0
97.0885.8
97.388955
97.7/94.1
98.2/95.7
95.3/85.9
96.5/914
97.5/94.1
PI+MI
93,6/82.2
96,4/90.0 97.5/92.7
94.7/73.0
96.5/8100 97.5/86.2
97.3/89.3 97.894.5 98.4/96.5
95.1/85.8
96.7/91.3
97.9/33.9
Table 2: Performance (Dice%) on different MRI modalities.
BRATS T1
BRATS TTC
BRATS T2
No. Clicks
5
10
10
3
10
ICNN
20.7
46.8
62.5
45.4
63.8
75.4
72.1
81.4
85.7
Med-SA
23.4
33.4
35.7
38.7
47.0
49.2
75.7
78.8
79.2
TA+SA
28.6
57.0
70.6
48.3
67.1
18.2
16.3
84.1
87.9
TSCA
34.4
60.9
74.0
50.1
70.2
79.6
77.7
85.8
89.0
61.1
72.4
78.9
64.3
74.7
80.4
82.5
PHMI
71.2
83.4
88.0
70.4
82.9
87.5
84,9
90.6
93.0
We set a- = 0.7 and 3 = 200, with o- = 3 in CCG loss, found adequate in preliminary experiments. The effect of different hyperparameter settings can be seen in Appendix A.3] We implement a base interactive model we denote as ICNN", using U-NET with the interactive method proposed by ICNN (Sakinis et al., 2019), and trained with our CCG loss. We implement IA+SA (Kontogianni al., 2020) and TSCA (Atanyan al., (024) using the same pretrained base interactive model (with CCG loss) as our method for fair comparison. For all online adaptation methods, all model parameters are updated during adaptation. In addition, we include SAM- interactive medical image segmentation model, the Medical SAM Adapter (Med-SA) (Wu et al. 2023b), which is fine- tuned on the source data and frozen during testing (target data).
Evaluation on Fundus data. We pretrain the models on REFUGE as the source distribution, for multi-class segmentation. We then adapt and evaluate using each of the 4 other fundus databases separately as targef distributions. Tab. [ shows the average Dice for both disc and cup. On G1020 and PAPILA, where the distribbiiin shift is large, our fast Post-Interaction method outperforms previous methods methods-especially especially on cups (disc segmentation is nearly perfect for most models and thus hard to amprove>-and all adaptation approaches surpass the frozen base model. On GSI and GAMMA, where the shift is small, our Post-Interaction method remains comparable or better. Using only the Dost-Interaction adaptation, which requires two back propaggapa,,,,,,s, already surpasses previous methods that need more than ten back-propagations Adding the Interatttnttion adaptation gives slightly better results in most cases. The improvement becomes much more significant when facing large data-distribution shifts in the brain-MRI databases.
We here adapt our model to scenarios with larger distribution shifts between different MRI modalities. The model is initially trained using the FLAIR scans of the training split. It is then adapted and evaluated on T1, Tlc, and T2 scans of the test split (separate experiment per modality). As shown in Tab. 2 all online-adaptation methods outperform the base interactive model, ICNN". Largest improvements shown in T1. Among online adaptation methods, our approach surpasses TSCA and IA+SA even with only Post-Interacio adaptation, especially when few clicks are given. Including Mid-Interaction adaptation yields even greater gains.
Evaluation on MRI Modalities:
addition, we test our model across different brain pathologies. We pretrain 2 models, one on BraTS Flair, and one on a combination of Flair/T//TIc. We then adapt and evaluate the first on TBI-Flair and WMH-Flair, and the second on TBI-TI and ATLAS-TI. Results are shown in Tab. 3 Even in these challenging settings, andine-adaptation meth- ods significantly boost performance, with our approach outperforming previous methods on all tasks. For TBI and WMH FLAIR, our Post- Interrnttaattion method achieves results comparable to TSCA after 10 clicks but attains higher dice scores with fewer clicks. After adding Mid- Interrataatton adapta- tion, our method achieves significantly better results. For TBI-T and ATLAS, Dost-Interaction alone
Adapting to Different Brain Pathologies:
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Table 3: Performance (Dice%) different brain pathologies.
Truined on BRATS(PImn
Trained on BRATS (Planr, TI. Tic)
TBI Flair
WWH
TBT
ATLAS
No. Clicks
10
10
10
10
ICNN
49.9
64.1
69.6
47.9
61.2
67.6
42.0
49.3
55.3
40.6
46.8
52.1
Med-SA
43.5
48.5
61.5
43.1
43.9
IA+SA
50.6
66.4
73.9
49.4
61.2
72.0
44.3
52.7
59.8
43.4
53.9
62.6
TSCA
52.7
66.1
73.7
52.8
66.7
72.7
44.4
55.8
63.9
43.4
55.7
64,0
Pl
53.8
68.8
73.6
53.7
56.7
72.3
47.7
61.1
68.0
62.7
77.0
81.8
PI-MI
55.2
69.9
76.3
59.0
73.0
78,9
47.7
67.0
74.8
66.4
82.2
86.0
Table 4: Adapting with maximum 5 or 3 clicks per image.
clicks
clicks
Method
T1
Tic
T2
Flair
Flair
T1
T1
ICNN
46.3
63.8
81.4
58.9
55.6
45.8
41.6
TSCA
62.9
69.0
86.0
61.9
59.5
52.9
51.4
PI-MI
80.4
89.5
90.3
65.3
64.3
54.6
73,3
significantly outperforms previous methods, and Mid-Interaccion further improves performance. Al- though the pseudo ground truth in carly iterations is suboptimal, as shown in the table (low Dice score for click), PI+MI still learn from and achieve higher scores.
We also observe that in all three tables, TSCA performs better than IA+SA, consistent with the pre- vious studies (Atanyan et al. 2024). Thus, we compare only with TSCA in subsequent experiments for simplicity. Furthermore, we observe that in most cases, Med-SA performs significantly worse than the base interactive model, ICNN, across all three tables, especially after 10 points. Therefore, we do not employ the computationally expensive SAM-based model further.
Adapting with fewer allowed corrections: All previous experiments used T 10 maximum clicks for correction of each image. However, method should ideally also perform well with fewer maximum performed corrective interactions. Here, we test our online- adaptation methods on brain MRI using maximum 7 = 3 or 5 clicks for interactive correction of each image. This also assesses the capability of our method to adapt using a less optimal pseudo ground- -truh. Tab. | shows results using 5 or 3 clicks max per image, under the same experiment settings as Tab, and Tab. Even with fewer clicks, anline-adaptation methods perform significantly better than the frozen model ICNN" Our Post-Inted Inst-lnterattion (PI) adaptation continues outperform previous methods in most cases. The addition of diid-nnteactton (PI+MI) further improves results. Although the model output after 3/5 clicks may be suboptimal, learning from it pseudo ground-truth remains effective.
Computational latency: We evaluated the computational latency of our method to assess its prac- ticality. On NVIDIA A5000 GPU, updates negligible (0.05s for MI; 0.09s for PI). Crucially, even on a CPU, latencies remain imperceptible (0.25s for MI; 0.41s for Pl). This ensures a smooth workflow, confirming that our simple, effective design is well-suited for real-world application.
Table 5: Ablation study by including different terms in PI and MI, after 5, 10 clicks. CCGLMI and DFLMI represent the Mid-Interaction adaptation. CCGLpi and DFLM represent the stage 2 of the Post-lnteaattion adaptation. S1 represent the stage of the Interacttoon adaptation.
Loss terms
G1020 (Cup)
ATLAS
BRATS TI
BRATS T2
DF
CCGL
DFL
CCGLa
SIm
82.2
90.0
92.7
66.4
822
86.0
71.2
8344
88.0
84.9
90.6
93.0
4
82.1
889
95.0
65.9
820
85.8
69.4
81.9
86.8
84.3
90.4
92.8
81.5
87.0
90.0
65.
80.1
42.3
46.6
84.6
90.4
8
4
81.9
885
92.2
62.7
77.0
81.8
61.1
72.4
78.9
82.5
88.1
90.9
82.
58.4
60.6
82.6
88.1
7
81.7
86.8
89.6
60.7
74.7
79.8
55.7
66.3
72.1
81.7
87.3
89.
81.6
87.7
91.4
59.0
73.0
78.7
48.6
61.3
67.9
80.9
86.4
89.4
Ablation Study: To evaluate the benefit of each component of our method, we conduct an ablation study each term of our online adaptation method. The results shown in Tab. [5] Dice scores are reported on four target databases: G1020 (cup), ATLAS, BraTS-TI, and BraTS- The source databases are follows: REFUGE2 for G1020 (cup), combination of BraTS Flair/11/Tic for
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ATLAS, and BraTS Flair for both BraTS-TI and BraTS-T2. The source-target pairs consistent with previous experiments. The ablation terms are divided into two groups: PI IPost-Interaction adaptation) and MI (Mid-Interacttin adaptation). SIpt is the first stage of the Post-Interaction adaptation approach. DFLpI and CCGLPt are the second stage of the Post-Interaction adapta- tion processes with the Dice-Focal loss Click-Centered Gaussian loss. DFLAI and CCGL are the Mid-Interaction adaptation processes with the Dice-Focal loss or Click-Centered Gaussian loss. Overall, the ablation study confirms the contribution of each component and stage in both the Mid-Interaction and Post-Intertn Interattraction approaches. The two loss terms should be used together in each process, For example, on the challenging BraTS T1 dataset where tumor boundaries are not well defined, performing MI solely with Dice-Focal loss can cause the model to learn incorrect information, as segmentation errors often extend beyond cancer boundaries. Adding the CCG loss effectively mitigates this issue by focusing learning on the corrected areas.
We have seen that MI adds benefits on top of PI. But does the opposite also hold? We evaluate whether adapting with PI offers benefits when MI is already performed. With budget of five clicks per image, Post-Interaction adaptation improves performance in nearly every scenario as show in Tab. 6 When the budget rises to ten clicks, Post-Interaction adaptation continues to provide substantial gains in the early iterations, but by the final click, its advantage narrows: WMH still benefits, while others do not. Exact numbers model leans more heavily on MI, which may partially cover the updates supplied by Post-Interaction adaptation. Even though the influence of PI may diminish with extensive interaction provided, helps users reach satisfactory results with fewer clicks, which is important for interactive workflows. We therefore recommend deploying both mechanisms in most situations.
given in the Appendix [A.4 With more clicks, the
Finally, we investigate aspects of the CCG loss in Tab. 7 Column "all" is the proposed version, all surrounding pixels), and
"no "no_class" removes the class-limited mechanism of CCG (applies "no gaussian" replaces the Gaussian kernel with a uniform kernel. Removing the Gaussian kernel or the class-limited mechanism reduces performance for both PI and PI+MI in most cases. Addi- tionally, we investigated different values of for the loss. As approaches zero, the loss effectively reduces to a single-point focus. Ablation study that explores the effect of this parameter is in Ap- pendix A.7 Results demonstrate the value of using Gaussian over focusing on a single point.
Table 6: Ablation study for PI under a 5-click budget. (Average Dice% over runs with different seeds.)
Table 7: Ablation study on the design of CCG loss. Performance shown Dice..
BRATS
WMH
TBI
all
no. class
DO gaussian
PI+MI
PT
PT
PI=MI
PI+MI MI
80.4
80.5
90.3
70.7
68.9
BRATS (T2)
93.0
90.9
92.1
87.7
90.6
90.1
78.4
79.8
89.8
68.0
69.0
WMH
78.9
72.3
77.6
73.1
72.2
69.8
No MI/II
46.8
63.8
81.4
61.2
64.1
AILAS
86.0
81.8
80.7
75.7
79.8
80.5
3.3
ROBUSTNESS AND OVERFITTING
In this section, conduct additional experiments assess the robustness of our method and po- tential overfitting, When plenty of clicks are provided for an image, the model may overfit to that image. To explore this, we consider an extreme case where each image receives 50 clicks (TSCA (50), OAIMS (50)). The result is shown in Tab. g In this scenario, TSCA (50) exhibits lower performance at the early clicks (e.g., click 1, 3) compared to TSCA(10), indicating potential overfit- ting to previously seen images. In contrast, our method performs significantly better with 50 clicks compared to 10, and does not exhibit signs of overfitting.
We also examine challenging scenario, where images from different Baaibaases-BraTie T1, BraTS T2, and WMH FLAIR- are randomly shuffled together, with each database contributing 25 images. The result is shown in Tab. g Despite substantial domain differences, method continues to outperform both the ICNN* and TSCA. Notably, while TSCA's performance approaches that of ICNN". our method maintains clear advantage. Removing Post-Interaction adaptation leads to performance drops. Hence our adaptation approach allows clinicians to use a single model that adapts to multiple diseases simultaneously, eliminating the need to manage multiple models.
Because our method uses model predictions as pseudo ground-truth, it is natural to wonder the process would accumulate errors and corrupt the model over time in scenarios when predictions

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Table 8: Performance BRATS T1 with budget of 10 or 50 clicks per image. Dice shown I. 3, 10, 20, and 50 clicks. Overfitting past images lowers Dice on next image with few clicks (1-3) using TSCA but not our method.
Table 9: Adapting database composed
of images from BRATS TI, T2, and WMH FLAIR.
No. Clicks
3
10
20
50
No. Clicks
5
10
ICNN
22.1
35.8
62.9
78.7
87.1
ICNN
50.1
65.6
74.3
TSCA(50)
28.9
49.6
75.3
88.4
92.9
TSCA
52.0
67.1
75.3
TSCA(10)
34.4
51.4
74.0
N/A
OAIMS(MI)
52.1
72.1
80.6
OAIMS (50)
73.9
81.8
89.8
94.3
95.9
OAIMS (10)
61.1
68.9
78.9
N/A
N/A
OAIMS(PI+MI)
55.5
73.7
81.6
bad or the user provides wrong clicks by mistake. We conducted experiments to assess this. First, to evaluate how the method performs when model predictions are of low quality (hence erroneous pseudo ground-truthe truth), we test scenario with extreme domain shift. Specifically, we pretrained the model using only BRATS Flair and apply it on Atlas TI. Results are shown in Table TO (top). This scenario represents a very large domain gap, where the base interactive model (ICNN*) without online adaptation, achieves very poor initial segmentation: with just localisation click, it achieves only 9.3% Dice. After 3 and 10 clicks, the ICNN* still performs very poorly (12% and 24.3% Dice respectively). although it does improve slightly with each click. We then apply online adaptation to the above model in 3 settings, when only 1, 3, or 10 clicks are allowed. Our method improves performance in all settings, and recovers high 80% Dice when using 10 clicks. To further assess robustness, we make this scenario even more challenging by ordering the images such that hardest images (lowest segmentation Dice) are presented first in the sequence. Here, the initial pseudo ground truth of the early images is very poor, with Dice near 0%. Results in Table (bottom)) show that performance of our method comparable to random ordering, indicating that extreme initial failures do not destabilize the model for subsequent images. Finally, we assess robustness to noisy input, such as in the case wrong user clicks. We simulated erroncous clicks by generating clicks in areas that the model assigned correct class, but assign the opposite class to the click (i.e. ask for wrong class correction). Results Table [ that our online adaptation method improves over non-adaptive ICNN", achieving good performance BRATS T1 when 40% of clicks are given the wrong class, when first 4 correction clicks per image are given wrong, or when for 40% of images all clicks are given wrong, demonstrating resilience against user errors.
Table 10: Robustness to pseudo ground-truth of bad quality due to extreme domain shift (top) and "worst case first" ordering (bottom).
Table 11: Robustness to noisy (wrong) input clicks.
No. Clicks
click
Aelicks
elllicks
Noise Type
5
10
ICNN' (No Adapu
9.3
12.8
24.3
40% clicks are wrong
ICNN*
20.6
32.9
43,9
TSCA
9.3
40.7
65.6
OAIMS
67.8
71.8
74.6
QAIMS (Ouns)
25.6
57.9
80.0
First clicks wrong each image 40% images get only wrong clicks
ICNN
N/A
13.3
43.3
No. Clicks
5
10
OAIMS
N/A
56.9
75.1
OAIMS (Random Order)
67.5
82.2
86.0
ICNN
21.0
40.4
53.8
OAIMS (Worst first)
66.4
81.6
85.3
OAIMS
68.1
75.2
76.9
CONCLUSION
This study investigates how to train and adapt an interactive segmentation model for medical imag- ing to better handle data distribution shifts. We proposed an online adaptation framework that tegrates both Post-Interaction and Mid- Mid-Interaction approaches, enabling the model to continuously adapt to new data distributions. A Click-Centered Gaussian loss is proposed, which enhances the model's responsiveness to user inputs. We demonstrate the effectiveness of our method through extensive experiments with diverse distribution shifts. The promising performance underscores the transformative potential of adaptive interactive segmentation in advancing both clinical practice and research applications. The methodology is amenable to other types of user inputs beyond clicks, such scribbles, and different types of backbone models, which could be explored in future work.
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ACKNOWLEDGMENTS
ZL. is supported by scholarship provided by the EPSRC Doctoral Training Partnerships programme (EP/W52431 1/1]. HA is supported by scholarship via the EPSRC Doctoral Training Partnerships programme BPPW5243311/L
1/1111 YI is supported by the EPSRC Centre for Doctoral Training in Health Data Science (EP/S02428X/1). The authors would also like thank Dr. Yu Liu for his valuable help on this paper.
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A APPENDIX
A.1 VISUALIZATION RESULTS
Fig. presents the visualization results demonstrating the adaptation performance on the BRATS
dataset. The segmentation map is overlaid in red the original image. Our OAIMS (PI+MI)
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GT
ISFCNN* *
TSCA
OAIMS (PI)
OAIMS (PI+MI)
ATLAS
BRATS T1
BRATS T2
Figure 3: Visualizations on BRATS Databases demonstrating adaptation to different modalities and pathologies (Trained on BRATS Flair for BRATS T1 and BRATS T2, Trained on BRATS Flain/T1/TIc for ATLAS
method produces segmentations that are closest to the ground truth (GT), with more accurate bound- aries compared to other methods.
Fig. fillustrates the databases used in our experiments. This visualization helps to better understand the distribution shifts across different databases and modalities.
Fig. 5 iillustrates how the predicted segmentation evolves across interaction clicks.
A.2 DETAILS OF THE SIMULATION PROCESS
To train and evaluate the interactive model, we simulate user interactions with an automatic point- generation procedure that places clicks in incorrectly segmented regions.
During training, we first generate a random click inside the target foreground object as localiza- tion click. Based on the resulting segmentation mask and the ground truth, identify incorrectly
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REFUGE2
GAMMA
G1020
Gs1
PAPILA
BRATS Flair
BRATS T1
BRATS T1c
BRATS T2
WMH Flair
ATLAS T1
Figure 4: Illustration of the databases used. The distribution across different datasets and modalities can be visually observed.
segmented regions with connected components. Each erroneous component ranked by size, and random point is generated within each. We then select the first K points from this queue, where K the desired number of clicks, and feed them into the model simultaneously. In our training, K is randomly sampled for each iteration from uniform distribution in the range 10].
At inference time, user clicks are simulated iteratively. First, one random click is placed inside the target foreground object. Then, based on the predicted segmentation and the ground truth, correction click is placed in the largest erroneous component (including both false positives and false negatives). A new segmentation is generated using all previous clicks, and the process repeats until a total of K clicks reached.
For the simulation process in the Post-Interaction stage, the procedure is similar to training. How- ever, instead of using the real ground truth, this step relies on pseudo ground-truth mask.
A.3 EFFECT OF DIFFERENT HYPERPARAMETER SETTINGS
In this section, we evaluate the effect of three hyperparameters, a, 3, and a, on the performance of method. The results are presented in Table 12
We observe that relatively small values of 3 (e.g., 100) and a (e.g.. 1) lead to noticeable performance drops the BRATS TI dataset. This suggests that overly small values can hinder the model's 's ability to effectively utilize the CCG loss. Therefore, we recommend selecting values greater than 100 for and greater than for a. The choice of also influences performance BRATS T1, while its
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Image
GT
click
2 clicks
clicks
clicks
10 clicks
$
4
4
4
Figure 5: Illustration of how the predicted segmentation from our OAIMS (PI+MI) method evolves
as more interaction clicks are provided. The examples are brain MRI images (ATLAS and BRATS
TI). From left to right: input image, ground truth, and predictions with 1, 2, 3, 5, and 10 clicks. The results show progressive refinement the segmentation as more clicks are provided.
impact on WMH minimal. We not specifically tune a to achieve the best results on BRATS TI.
Overall, performance remains stable most cases. Note that in our experiments, when comparing with other methods, we did not perform hyperparameter tuning to obtain the best-performing figuration for any specific database. This decision was made due to the relatively stable performance of our method across tasks and to better demonstrate its robustness.
Table 12: Dice scores (%) on WMH and BRATS TI under 10-click interaction budget. Each value of a, B, and a tested in combination with all values of the other two hyperparameters (i.e., a x 4 3 3 total combinations). The reported score for parameter's value is the average over all combinations that include
Parameter
Value
BRATS T1 (%)
WMH (%)
0.3
85,6
77.7
0.5
87.0
77.6
0.7
84.3
78.3
100
81.3
77.5
200
86.9
77.9
B
300
87.2
78.1
400
87.4
78.1
1
81.0
77.2
87.9
78.2
5
88.1
78.3
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A.4 ABLATION STUDY ON PI (10 CLICKS TOTAL)
As supplementary information the main paper, provide the numerical results of the ablation study of Post-Interaction (PI) adaptation under budget of 10 clicks. The results are shown in Tab. 13 PI continues to offer substantial performance gains in the early iterations; however, by the final click, its advantage diminishes- WWG still benefits, while other methods do not. We continue recommend deploying both mechanisms in most situations, with further explanation provided in the main paper.
Table Ablation study for Pl with 10-click budget. Dice shown at 1, 10 clicks. Pl and MI are the proposed Post-Interaction process and the Mid-Interaction process of our method.
PI+MI
MI
Dataset
3
10
10
BRATS T1
71.2
79.4
88.0
69.1
77.7
87.6
BRATS Tic
70.4
78.9
87.5
68.3
77.9
87.7
BRATS T2
84.9
88.6
93.0
84.6
88.6
93.0
WMH Flair
58.9
68.6
78.9
58.4
68.1
78.0
TBI Flair
55.2
65.6
76.3
53.3
64.4
76.4
A.5 DICE SCORE
To evaluate segmentation performance, we use the Dice score. It measures the overlap between the predicted segmentation mask P and the ground truth G, and is defined as:
2|P Dico (P. = [P] G 2TP FP + FN
2TP
(6)
Here, TP, FP, and FN denote the number of true positives, false positives, and false negatives, respectively. higher Dice score indicates a greater overlap between the prediction and the ground truth.
The Dice score is particularly well-suited for medical image segmentation, as emphasizes accurate delineation of foreground regions- sucon as lesions-which are often small and sparse. As result, we adopt Dice as our evaluation metric.
A.6 ALGORITHMIC DESCRIPTION
We here present the algorithmic details of the source training procedure (Algorithm) and the test- time inference/adaptaatin loop (Algorithm[I)
Although Algorithm Iis formulated with fixed number of interactions (T) for simplicity. consistent with the main paper, in clinical practice, the user can provide any number clicks for each image until they satisfied.
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Algorithm Online Adaptation for Interactive Segmentation (OAIMS)
1: Input:
2: f: Base interactive Model
3: 0*: Pretrained parameters
4: S: A sequence of images I
5: T: Number of ver-interaction clicks
6: < 0"
7: for I S do
Process each image sequentially
Inference and Mid-Interaction (MI) Adaptation
8:
9:
a f LocalizationCCick([]
> Get localization click from target foreground object
10:
Ct {a}
11:
Pi - s(I,C0)
for / to
13:
a + Getelick(PP-111) CuCufat
User provides new click
15:
Get Pinitial for updating the model (no gradient calculation) LMI t LDD Pinitial) + (P-cc Pinital Ct.
Pinitial t- f(I,C;0)
17:
- UpdateParameters LMT
18:
P. - f(,,C;0)
19:
end for
20:
Pj Pjimal + Pr
Final mask after T clicks
Post-Interaction (PI) Adaptation stagel
21:
C+ {er)
D. Get first click from MI
22:
Pi ((T,C;0)
23:
Cslm + LDF(
+t Update Pardmttearammters(( Lsim)
Post-Interaction (PI) Adaptation stage2
25:
C + GenerateClicks((, (P1, Pfinat, T)
Generate on each erroneous component (up to T)
26:
P P ((I,
Cg2n + (P, + BBCocooPP.... Pyénal: C)
28:
I + "UUdateParameteen L5am
end for
Algorithm 2 Pretraining the Interactive Model
Input:
2: f: Base interactive Model
3: Doouree: Source (training) dataset (e.g. REFUGE). (I, Mgt): Image and ground-trute -rrth mask
4: Tmax: Max number of simulated clicks (e.g. 10)
5: for Mgt) E Duource do Cloe + SimulateL SaanulateLocalzzatinnCCckC,,,, Pioe f f(1, {Cioc);
6:
7:
D> Get prediction from localization click
8:
E f- FindErroneousReggionss (Ploc: Mgt)
> Find all erroneous connected components
9:
Cranked x- RankAAndGeenerateCCiieee
Rank components by size, randomly generate one click in each component Sample K from discrete uniform distribution b Select the top error clicks
K U11,
11:
C - SelectFirstM (Cranked; K)
12:
P. - ()
13:
C (Dr(P.N) Ma.) LocolP, Mge. C)
14:
B + Padateearameteers(...
15: end for
16:
return
17

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Published as a conference paper at ICLR 2026
A.7 ABLATION STUDY OF IN CCG LOss
We conducted an ablation study on the BRATS-TI dataset by varying the standard deviation o. We evaluated E {0, 2, 3, 4, 5), where the 0 case was implemented applying the CCG loss only on the single pixel. The results are illustrated in Figure 6
Zgna VINA
Figure 6: Ablation study of the Gaussian parameter a of CCG Loss on the BRATS-TI dataset.
As a increases from 3, the performance improves significantly, as the model receives more information from surrounding pixels. When is large enough (e.g.. 2 3) the method seems robust the choice of value, achieving stable performance.
We also observed that for 0 the adaptation was highly unstable, sometimes resulting in very low performance. For example, over runs (RNG seeds) of the same experiment, the final score was 79.9, 46.9, and 83.2. This instability was only observed for = O. This is likely because the model can easily overfit a single pixel, which can strongly hinder performance.
A.8 ASSESSING NUMBER OF CLICKS TO REACH TARGET DICE
To further evaluate the clinical efficiency of our method, measure number of clicks to reach specified Dice score, which assesses how quickly user can achieve satisfactory segmentation.
In this experiment, set target Dice score of 80% with maximum of 20 clicks per image. If an image failed to reach the target within 20 clicks, its click count was capped at 20. We compared our proposed method (PI+MI) against the strongest baseline, TSCA, on the BraTS dataset (transferring from Flair to T1, Tlc, and T2).
The results are presented in Table 14 Our method requires significantly fewer clicks reach the target accuracy across all modalities, demonstrating superior efficiency in correcting domain shifts.
Table 14: Average number of clicks required to reach 80% Dice (lower is better), with maximum of 20 clicks per image
Method TSCA
BRATS TI
BRATS TIC
BRATS
10.60 4.43
8.71
3.63 4.59
PI+MI (Ours)
2,33
A.9 COMPARISON WITH APPLYING UNSUPERVISED LOSSES ON MODEL PREDICTION
Given that our method is based on the idea of using the user-refined model predictions as pseudo- labels, one may wonder if would be sufficient simply minimize Cross-Entropy (CE) between the model prediction and the pseudo-label, method often used for learning from unlabeled data Lee et al. (2013). Specifically, assume for an input image I, after t corrective clicks by the user, the model with parameters 0, predicts P. I WW K the class-probability maps (output of softmax in our model) and P.- = argmax((p)) E R Nx the predicted segmentation. Here, and W are the height and width of I and these maps, and K the number of classes in the segmentation task. Then, p e # is the class cluss-probabilityy map for class k. pf (i.j) E [0, 1] the probability that the pixel with coordinates (i,j) is of class k and ji, the predieted class for the pixel. Then,
18

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Published as a conference paper at ICLR 2026
to optimize model parameters 8,, this method minimizes for each image the Cross Eotso-y loss between predicted class probabbibbbilsss pt and the pseudo-labe mask Pa:
CCE - pf
where [A] is the indicator function that takes value 1 if A is True, i.e. only for l equal to the predicted label) and otherwise.
Another very related method is & Bengio (2004). Using similar notation as above, the Entropy loss for an image is defined as:
for learning from unlabeled data Grandvalet
Lr
ΣΣ (o.r))) 3.
(8)
We experiment with these methods by replacing our own methodology during PI and MI stages instead of methodology. For PI, we process one image time for OAIMS) and after T user corrections per image, we obtain the final segmentation predicted by the model Pinal Pr and associated probability maps pr- We then apply an update model parameters, similar in our method, by minimizing one of the two above losses (using Pr as pseudo-label in CE, whereas EM does not require it). We then experiment with using these losses both during MI and PI. this case, after each user click t, we optimize one of the above losses, and after all T corrections were completed, the losses are also optimized for PI as above.
We performed these experiments based on the Fundus and BRATS datasets. Hyper-parameters of these unsupervised methods were optimized via cross-validation to get optimal performance from them. As shown in Table [5] these losses provide almost no improvement and sometimes even yicld worse performance than our non-adaptive backbone model (ICNN*). This is likely because the information gained from unsupervised losses is limited. Our method, OAIMS, outperforms consis- tently, especially with fewer clicks. When the distribution shift larger, between BRATS datasets (6) Online learning methods such as IA+SA and our OAIMS outperform unsupervised losses in all cases with much greater difference 5-60% Dice dependent on setting), especially on BRATS TI and Tlc. These experiments show that these unsupervised losses by themselves cient. Although they take advantage of the corrected model predictions. they do not explicitly use the user clicks. Instead, it is valuable design frameworks that effectively leverage signal from user interactions to adapt to new data, like our OAIMS framework that explicilly emphasizes areas around user clicks via CCG.
not suffi-
Table 15: Performance on fundus imaging.
PAPILAA
GSI
GAMMA
ICNK
89.077.44
93.18551
95.155 1:3.00
38.356133
92.5/0.2
91.777.1
95.N82.4
21904
99.791
91.0813
96.179933
97.293.3
Exnapyy-Mi Min Pl
89.4/77.2
93.185.11
95.067.9
88.78604
92.7/90.4
33.9977.3
96.7/22.5
07 222290.
97.784.11
U4.49335
96.1899.6
97.2/9..0
Ennapy. Min MIsPI
89.7/77.7
93.328510
95. 25.1/
89.6661 2
93.3/70.4
95. 1766
96.692.1
97. 1.11.00
97.693.7
94.5983.
96.1/99.7
97.2/5.0
Cross Cross-Enrog Enampy Pl
89.2/7.4
95.1885.1
95. 95.1/88.2
88.3860L3
92.67003
94.876677
96.692.6
97. 190.8
97.694.1
91.083.5
96.2/99.6
97.1/93.0
Cross Enmopy Mi+PI
89.6/7777
95.3883.3
95 23.0987.4
39.3861.1
93.17905
17668
96.783377
97. 190.7
97.383.9
94.38838
96.2/99.9
97.1993.1
IAASA
00.5/77.4
94.3884.5
96. 15003
89.4811
93.3/70.5
377.6
96.883.44
07 491.5
98 09.09488
94 78400
96.3990
97.4/33.9
TSCA
89-0777.
94.28550
96. 150.7
89.6461.7
94.072.2
96. 179.0
96.985.44
07. 592.9
98 09.0995.
96.4910.
97.59941
OAIMS (PH+MI
03.6/2.2
96.4M00
97.5922.7
94,77730
96.5/81.0
97.5/662
97.389.3
97.894.
98.4%6.
95.0
96.7/91.3
97.0933.9
Table 16: Performance different MRI modalities.
BRATS T1
BRATS Tic
BRATS T2
No. Clicks
5
5
10
5
10
ICNN
20.7
46.8
62.5
45.4
63.8
75.4
72.1
81.4
85.7
Entropy-Min PI
20.1
48.2
65.6
45.4
65.3
76.3
72.6
82.0
86.5
Entropy-Min MI-PI
38.1
42.3
57.8
66.8
81.2
86.8
Cross- Entropy
20.0
49.7
66.0
46.6
65.6
76.7
72.5
82.1
86.6
Cross-Entropy MI+PI
42.4
42.5
59.6
82.6
81.3
IA+SA
28.6
57.0
70.6
48.3
67.1
78.2
76.5
84.1
87.9
TSCA
34.4
60.9
74.0
50.1
70.2
79.6
77.7
85,8
89.0
OAIMS(PI-MI)
71.2
83.4
88.0
70.4
82.9
87.5
84.9
90.6
93.0
19

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Published as a conference paper at ICLR 2026
A.10 EXPERIMENTS ON SOURCE-LIKE DATA
We here conducted additional experiments to evaluate our method's performance on source-like data, ensure the adaptation does not degrade performance the absence of distribution shift.
First, we tested the model directly on the exact domain it was trained on (source domain): Train on BraTS Flair (Train set) + Test on BraTS Flair (Test set). In this "no shift" scenario, we want to ensure that the adaptation mechanism does not degrade performance. As shown in Table [7] both our PI and PI+MI variants perform similarly to the non-adaptive baseline ICNN* demonstrating the stability of our method.
Table 17: Performance on Source Domain (Minor Shift). Trained and Tested on BraTS Flair.
No. Clicks
10
ICNN
93.4
95.6
96.4
Ours (PI)
93.4
95.5
96.3
Ours (PI+MI)
93.3 95.8
96.6
Second, assessed scenario to see if the model could regain performance on the source domain after adapting to new domain. We first trained a model on BRATS Flair (train set), then adapted it different dataset (TBI Flair), and then applied it back to the original source domain (BraTS Flair). We compare this with performance of our backbone model (ICNN*) without adaptation. The results are shown in Table I8 When OAIMS adapts model parameters to TBI, the middle database, but then we do not allow it to re-adapt to the source data when it's tested on them (No Re-adaptation), shows a slight drop in performance, especially with 1 click (90.5% vs 93.4%). However, if we allow OAIMS to re-adapt to the source data BRATS Flair via standard online learning process (OAIMS (Re-adapt)). it recovers its original performance well, even with just click of interaction.
We note that in practice, perhaps the most practical scenario are distribution shifts between training data and the database of deployment. Therefore, we are primarily interested in learning to adapt and perform well the new domain, whereas continuing perform well on the training domain is lower priority. However, this experiment shows the flcxibility of OAIMS and its effectiveness to rd-pt to the original data easily and effectively.
Table 18: Performance on BraTS Flair after adapting TBI.
No. Clicks ICNN*
5
10
93.4
95.6
96.4
OAIMS (No Re-adapt)
90.5
94.8
95.7
OAIMS (Re-adapt)
93.5
95.8
96.6
A.11 DATA EFFICIENCY OF OUR ONLINE ADAPTATION METHOD
To evaluate the data efficiency of our online adaptation method in unseen domains, we analyzed the number of samples required to reach satisfactory segmentation performance. For each target dataset, we partitioned the data into an adaptation split and a held-out evaluation split. Online adaptation was performed only on the adaptation split.
During the adaptation phase, the model was sequentially updated using images from the adaptation split, with 10 clicks per image. To track the efficiency of this process, we evaluated the model's performance on the held-out split after every five adaptation samples. During this evaluation step, we provided 10 simulated clicks for each image in the held-out split, without adaptating them.
The results are visualized in Figure 7 where the x-axis represents the number of images processed for online adaptation, and the y-axis reports the average Dice score across all images the held- out set. The left subfigure presents results of model pre-trained on BraTS (FLAIR, T1, Tic) and adapted/evaluated on ATLAS, where the adaptation split and held-out split contain 554 and 100 images respectively. The red reference line indicates the in-distribution performance of automatic baseline trained offline with standard supervised learning on the entire 554-image adaptation split, and evaluated the held-out set from the same distribution. This gives useful reference for what performance is meaningful to achieve by a model that was trained on a different distribution after online adaptation the target distribution. The center subfigure shows results the BraTS T1
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Published as a conference paper at ICLR 2026
test split using model pre-trained on BraTS FLAIR. In this case the adaptation split contains 199 images and the held-out split contains 50 images. The right subfigure shows results on the WMH FLAIR using model pre-trained BraTS FLAIR. In this case the adaptation split contains 40 images and the held-out split contains 20 images.
347
0.01
2071
0/77
400
0.0
maague
Figure 7: Online adaptation on ATLAS (left), BraTS TI (center) and WMH FLAIR (right) datasets. The Dice score is calculated corresponding held-out sets after every five adaptation samples. Online adaptation with our method reaches high performance to the new domain after processing only few (10-40) images, reducing the effort needed by the user for subsequent corrections.
As shown in Figure 7 the model exhibits high data efficiency. On the ATLAS dataset (left), the learning curve demonstrates steep initial ascent; after adapting only to 5 images, the interactive model's performance on the previously unseen target distribution already surpasses that of the auto- matic baseline trained on this distribution. As the sequential adaptation progresses to approximately 30 images, the average Dice score stabilizes above 0.80. This represents substantial gain over the pre-adaptation baseline score of <0.55 (at images), clearly demonstrating the method's ability to quickly adapt to an unseen domain. The BraTS T1 results (center) show similar trend: the interac- tive model outperforms the baseline after just 15 images and stabilizes after 40 images. The WMH FLAIR results (right) also show that the interactive method quickly outperforms the baseline (after just images) and achieves high performance with fewer than 40 cases.
higher performance level
A.12 USE OF LLMS
We used large language model (LLM) only to improve the writing. Specifically, the LLM was employed revise some sentences, focusing on grammar and style. The LLM was used for generating ideas, searching related work, or contributing to the scientific content of the paper.
21


Reviews:
[{"review_id": "f9MDU6RSDh", "reviewer": "Reviewer_KBrJ", "summary": "This paper addresses distribution shifts in medical image segmentation by using interactive segmentation as an online adaptation mechanism. The authors propose a framework where user click inputs not only guide predictions but also serve as signals for adapting model parameters during deployment. The method consists of two adaptation components: Post-Interaction adaptation (updating after complete image refinement) and Mid-Interaction adaptation (updating after each individual click), both incorporating a Click-Centered Gaussian loss that enhances the model's responsiveness to user guidance. They state that the user-corrected outputs can be treated as pseudo-ground-truth labels, enabling the model to continuously learn from sequential test images without additional manual annotations. The framework strengthens click responsiveness during initial training and focuses learning on clinically relevant regions indicated by user interactions. Comprehensive experiments across various medical imaging datasets demonstrate consistent improvements over existing methods in handling distribution shifts, including unseen imaging modalities and novel pathologies. This work aims to provide a practical solution for deploying segmentation models in medical imaging environments where domain shifts are common and user interaction is already an integral part of the clinical workflow.", "questions": "The role and necessity of multiple correction points are unclear, especially if Stage‑1 user clicks are already assumed to be pseudo ground truth.\n\nThe introduction of artificial clicks risks injecting label noise during re‑training under the stated loss functions, and this potential degradation should be analyzed and controlled.\n\nThe click‑centered Gaussian loss lacks sufficient technical detail, and the paper does not provide empirical insights or ablations to explain its behavior and sensitivity.\n\nThe use of pretraining and its fidelity to real deployment are under‑specified, including whether the source model was trained with extensive click supervision and which loss functions were used.\n\nA concise algorithmic description for both source training and test‑time inference with precise mathematical notation would significantly improve reproducibility.\n\nExtending results to adaptation‑oriented segmentation benchmarks would strengthen claims of generality and applicability.\n\nIn Table 5 for BraTS, the inclusion of CCFL yields a large jump from 46.6 to 81.9, whereas similar gains are not observed elsewhere and are only marginal on BraTS T2, which warrants deeper analysis.\n\nTwo additional baselines would strengthen the study, namely standard cross‑entropy and entropy‑minimization losses applied at test time with pseudo labels or model predictions.\n\nClarification is needed on which network layers are updated during adaptation, given the combined image‑and‑click inputs and their contribution to re‑training effectiveness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "The paper’s direction is interesting, and treating the post‑interaction, user‑refined model as a pseudo ground truth is a thoughtful design choice.\n\nThe evaluation spans multiple levels of distribution shift and reflects realistic clinical scenarios in medical imaging.", "weaknesses": "The submission would benefit from greater technical depth, as the current contribution focuses primarily on the overall framework and the click‑centered Gaussian loss, which, on its own, is not fully convincing.\n\nMoreover, I attempted to explore the advantages of the proposed method further. I have been trying to consider whether simply using an unsupervised loss based on the user’s inputs during inference would have improved the performance.", "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": "sSpDgbmM7m", "reviewer": "Reviewer_MLug", "summary": "This paper proposes an online learning technique for interactive image segmentation in the medical domain, where they specifically propose it for domain adaptation (which is one of the main problems in medical imaging). In particular, the main innovation seems to be a Click-Centered Gaussian loss function, which is a loss which acts on interactive clicks and makes the model react more quickly to user-clicks than previous methods. This loss is applied in a pre-training phase (out-of-domain pre-training phase), a Mid-interaction phase (i.e. between user-clicks in a single image), and a Post-interaction phase (i.e. after a single image is segmented). A well designed set of experiments show that the proposed loss is important in all three training phases, that their effects are cumulative, and that their final method outperforms the state-of-the-art.", "questions": "* Is it possible to ablate the Sigma for the Gaussian points?\n* Is it possible to do a small user study as described above?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The Click-Centered Gaussian loss function is simple: It takes a user click, does a Gaussian convolution, and uses this as weights for a cross-entropy loss. \n* The CCG loss makes sense: by focusing only on the area around the user click, it encourages the model to quickly respond to user interactions. In contrast to previous user-click losses which only operate on a single pixel, the Gaussian ensures that a larger portion of the image is taken into account which experiments demonstrate to be beneficial. Note that I prefer simple and effective methods, since these are more likely to get actual adoption in the field.\n* The authors compare with two previous methods (IA+SA and TSCA) on six different datasets and convincingly show that their method works better.\n* Stage two (page 4) of fine-tuning with multiple correction points is well explained how it avoids trivial network updates.\n* There are a good amount of ablation studies to better understand the behavior of the proposed method.", "weaknesses": "* There is not a single study with a real human: typically a simulated user behaves differently from a real user. The paper would improve if there is at least on real human study on a single dataset where the authors show their method works. This would also entail having a variable number of user interactions during annotation. Ideally this should be compared to a similar experiment using the best competitive method TSCA.\n* The sigma of the Gaussian is not ablated. Theoretically, a sigma close to zero goes back to having the loss only on a single point. A sweep over sigmas could help understand how much the ‘Gaussian’ part of the loss really contributes.\n* There seems to be a contradiction in the writing: In stage 2 the authors argue that it is not possible to reuse the user’s original correction clicks to update the model as the model already yielded the final prediction using those corrections, leading to trivial updates. Then in stage 3 they seem to do exactly that: using the last click c_t to reinforce the resulting prediction p_t^initial. While I think both are valid, the way it is written now is confusing since it directly contradicts the previous paragraph. Also, it took me quite some time to figure out why p_t^initial has the ^initial modifier. Some careful rephrasing in both stage 2 and 3 are needed to solve this problem, but this should be easily doable.\n* Mid-interaction and post-interaction learning are not new (e.g. IA+SA). But this method works better.\n* Using Gaussians for points is also not new, but it is shown to be very effective in this context.", "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": "Xb051Wq6tS", "reviewer": "Reviewer_n4wo", "summary": "This paper proposes an online adaptation framework for interactive medical image segmentation under distribution shift. The method, termed OAIMS, introduces two adaptation mechanisms, Post-Interaction (PI) and Mid-Interaction (MI), that update model weights during and after user interaction sessions. The approach employs a Click-Centered Gaussian (CCG) loss to strengthen responsiveness around user clicks and to improve model generalization across unseen modalities or pathologies. The authors evaluate their method on multiple fundus and brain MRI datasets, demonstrating superior performance over existing online adaptation methods such as IA+SA and TSCA.", "questions": "1. How computationally expensive is the online adaptation per image? Could this realistically run in real-time clinical use?\n2. Have the authors tested their method with actual human annotators rather than simulated clicks?\n3. How sensitive is the method to noisy or inconsistent initial inputs?\n4. Could the pseudo–ground-truth mechanism lead to error accumulation (catastrophic drift) during long adaptation sequences?\n5. How does OAIMS behave under minor distribution shifts, does continual adaptation degrade performance on source-like data?\n6. Compared to strong non-online interactive models (e.g., nnInteractive), does online learning provide a clear advantage?\n7. The method focuses on point clicks, can CCG also adapt to other interaction modalities such as bounding boxes or scribbles?\n8. The paper claims PI adaptation provides benefits even when MI has already been applied; however, if MI is trained on one modality, can MI alone be applied on a new modality without retraining PI and still be effective?\n\nThis is a solid paper, and if the authors can address these questions, I would consider increasing my score.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper tackles a practically important problem, handling distribution shifts in medical image segmentation, by combining interactive segmentation with online adaptation.\n2. The proposed OAIMS framework, integrating Post-Interaction and Mid-Interaction updates, is conceptually clear and technically well-structured.\n3. The Click-Centered Gaussian (CCG) loss is an intuitive and effective design that enhances model responsiveness around user inputs.\n4. Experiments cover a wide range of datasets (five fundus and four brain MRI), demonstrating consistent cross-domain generalization.\n5. Comprehensive ablation studies verify the contribution of each component and show the robustness of the method.\n6. The paper is clearly written, well-organized, and supported by informative visualizations that make the method easy to follow.", "weaknesses": "1. The method is only validated on a U-Net backbone; generality to Transformer-based or large segmentation models (e.g., SAM) is not demonstrated.\n2. The experiments lack a practical interaction-efficiency metric (e.g., number of clicks to reach 80% IoU / 80% Dice), which is important for interactive workflows.\n3. The pseudo–ground-truth assumption is strong, and if the model’s initial predictions are highly erroneous, online updates may accumulate errors and cause distributional drift.\n4. The baseline comparisons omit the latest (2025) interactive segmentation methods; the novelty claim for CCG is overstated given related prior work (e.g., methods that weight click neighborhoods or modify attention such as AdaptiveClick (TNNLS, 2025)).\n5. Computational cost and latency of online updates are not reported, leaving clinical feasibility unclear.", "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": "tn27a5JXZs", "reviewer": "Reviewer_oQJi", "summary": "This paper presents an online adaptation framework for interactive medical image segmentation to address distribution shifts.\nIt introduces 2 types of adaptation: Post-Interaction and Mid-Interaction adaptation. A Click-Centered Gaussian loss is proposed to control how the model adapts to signals around user clicks.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed framework, combining Post- and Mid-Interaction adaptation, along with the problem formulation, which integrates model updates with interactive segmentation, is interesting.\n\nThe CCG loss is a reasonable design choice and can effectively prevent the model from learning pixels' labels irrelevant to the click, focusing updates on relevant regions.\n\nExperiments are comprehensive, covering fundus and brain MRI datasets. Experiment results are promising, demonstrating significant performance gains over existing methods, especially under large distribution shifts", "weaknesses": "It would be helpful if the authors could provide more implementation details on finetuning e.g., whether the entire U-Net is made trainable during adaptation or if some layers are frozen.\n\nIt would be helpful if the authors could explain why focal loss is used. Is the training data overwhelmed with easy-to-learn regions?\n\nMid-Interaction adaptation seems intimidating, because the user has to wait for the model to be updated before providing another click, which could introduce latency and disrupt workflow.\n\nWith only one gradient update step, it is possible that the updated model still predicts the wrong label for the user-clicked region. It would be helpful if the authors could provide some discussion.\n\nThe method's effectiveness relies on a reasonably good pseudo-ground-truth. In cases with very few clicks or extremely poor initial predictions, this assumption may not hold.", "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)