TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Authors: Chuang LIU
Hongyan Xu
Yichao Cao
Xiu Su
Zhe Qu
Tianfa Li
Shan An
Haogang Zhu
Venue: ICML 2025 poster (2025)
Keywords: medical image segmentation
generalization
vision foundation models
Primary area: applications->health_medicine

Abstract: Medical imaging faces significant challenges in single-domain generalization (SDG) due to the diversity of imaging devices and the variability among data collection centers. To address these challenges, we propose \textbf{TinyMIG}, a framework designed to transfer generalization capabilities from vision foundation models to medical imaging SDG. TinyMIG aims to enable lightweight specialized models to mimic the strong generalization capabilities of foundation models in terms of both global feature distribution and local fine-grained details during training. Specifically, for global feature distribution, we propose a Global Distribution Consistency Learning strategy that mimics the prior distributions of the foundation model layer by layer. For local fine-grained details, we further design a Localized Representation Alignment method, which promotes semantic alignment and generalization distillation between the specialized model and the foundation model. These mechanisms collectively enable the specialized model to achieve robust performance in diverse medical imaging scenarios. Extensive experiments on large-scale benchmarks demonstrate that TinyMIG, with extremely low computational cost, significantly outperforms state-of-the-art models, showcasing its superior SDG capabilities. All the code and model weights will be publicly available.

TinyMIG:
Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Chuang Liu 12 Hongyan Xu
Yichao Cao
Xiu Su
Zhe Qu
Tianfa Li
Shan An
Haogang Zhu 126
Abstract
DAPSAM
85
TinyMG (Ours)
Rein (Dinov2)
80
Medical imaging faces significant challenges in single-domain generalization (SDG) due to the diversity of imaging devices and the variability among data collection centers. To address these challenges, propose TinyMIG, framework designed to transfer generalization capabilities from vision foundation models to medical imag- ing SDG. TinyMIG aims to enable lightweight specialized models to mimic the strong general- ization capabilities of foundation models terms of both global feature distribution and local fine- grained details during training. Specifically, for global feature distribution, we propose a Global Distribution Consistency Learning strategy that mimics the prior distributions of the foundation model layer by layer. For local fine-graing ree-ggrandd de- tails, we further design a Localized Representa- tion Alignment method, which promotes seman- tic alignment and generalization distillation be- tween the specialized model and the foundation model. These mechanisms collectively enable the specialized model to achieve robust performance in diverse medical imaging scenarios. Extensive experiments on large-scale benchmarks demon- strate that TmnyMIG, with extremely low com- putational cost, significantly outperforms state- of-the-art models, showcasing its superior SDG capabilities. All the code and model weights will be publicly available.
75
Rein
MedSAM
TriD
Dice (%)
(SAM)
SLAug
70
65
CSDG
60
DeepAll
55
70M
50M
30M
15M
50
60
50
40
30
20
10
GFLOPs
Figure 1. The proposed MIG significantly surpasses the SDG capabilities of SOTAs with extremely low computational cost and model parameter size.
1. Introduction
In the field of medical imaging analysis, Domain Adaptation (DA) (Kouw & Loog, 2021; Guan & Liu, 2021a) and Do- main Generalization (DG) (Zhou et al., 2022a; Yoon et al., 2023; Huang et al., 2020) address the critical challenge of managing variations in data distributions that deviate from the conventional assumption of independent and identically distributed (IID) data (Carlucci et al., 2019). Among these, Single-source Domain Generalization (SDG) tackles the practical problem of developing methods capable of gener- alizing from single source to multiple out-of-distribution (OOD) target domains (Liu et al., 2022b; Xu et al., 2023), This task is especially relevant in medical imaging, where data availability is often limited, and privacy requirements are stringent (Price & Cohen, 2019). In recent years, key approaches SDG have been proposed include Data Aug- mentation (Liu et al., 2024; Zhou al., 2021; Su al., 2022b), Regularization (Chen al., 2022: Zhang al., 2022), and Self-supervised Learning (Zhou et al., 2022b). each of which aims to improve model robustness and adapt- ability to unseen domains. Despite these ad advancements, sig- nificant challenges remain in comprehensively addressing the SDG problem, such as limited generalization capabil- ities and high computational complexity, highlighting the
'Equal contribution Sttte Key Laboratory of Complex Critical Software Environment, Beihang University, China Zhongguancun Laboratory, China 'Big Data Institute, Central South University, China *Jinan Institute of Supercomputing Tech- nology 'Shooo of Electrical and Information Engineering, Tian- jin University, China 'Hangzhou International Innovation Insti- tute. Beihang University, China. Correspondence to: Xiu <xiusu esu.edu.cn>. Haogang Zhu <haogangzhu @ www..cdu.cm.........mmmocbbmu
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
ongoing need for further research in this area.
For local fine-grained details, we design a domain- invariant representation transfer method, which achieves improved localized representation alignment through trequeney-domail discriminative content en- hancement.
Regarding model generalization capabilittis, one of the most notable recent advancements in computer vision (CV) and natural language processing (NLP) has been the develop- ment of Foundation Models (FMs) or Large Models (LMs), such as CLIP (Radford et al., 2021), SAM (Kirillov et al., 2023b), and GPT (Brown et al., 2020). In this work, we prefer to use the term Vision Foundation Models VFMs to refer to these methods. Leveraging large-scale parameters and extensive pre-training datasets, these models demon- strate superior generalization capabilities across a range of downstream tasks. The latest developments in these areas inspire us to reflect on two key questions: i): Whether the generalization capabilli capabilities of VFMs can enhance smaller specialized models, particularly in specialized fields such as medical imaging. ii:: Additionally, how to design effective knowledge transfer methods to facilitate the improvement of specialized models by leveraging VFMs.
Extensive experiments on public datasets demonstrate that TinyMIG significantly outperforms existing meth- ods with minimal model size and computational cost.
2. Related Work
Domain Generalization (DG), DG has been prominent re- search focus in the medical imaging field (Wang et al., 2020; Guan & Liu, 2021b; Su et al., 2022a; Butoi et al., 2023). Var- ious approaches have been proposed, including image-level data augmentation techniques like style diversification (Liu et al., 2020b; Su et al., 2021a; Zhang et al., 2020; Su et al., 2022b), and feature space manipulation methods based on adversarial learning or statistical randomization (Chen et al., 2022; Zhou et al., 2022b; Chen et al., 2023; Ouyang et al., 2022a). Moreover, disentanglementt-eese been explored to promote cross-domain feature invariance (Hu et al., 2023; Gu et al., 2023). Recently, spectral de- composition has been shown to effectively capture style and content information, which are closely associated with am- plitude and phase spectra (Liu et al., 2021; Xu et al., 2021; Wang et al., 2023b), in the context of DG. This observation motivates our investigation into the efficient transfer of gen- eralization features from VFMs to lightweight specialized models within the frequency domain.
In this work, we propose MInyMIG as shown in Fig. 2, a method to transfer generalization proficiency from VFMs to medical imaging SDG. The core objective of this frame- work is to enable smaller specialized models to mimic the strong generalization capabilities of large foundation models in terms of both global feature distribution and local fine- grained details during training. so that the specialized mod- els can independently handle domain shifts in unseen target domains during testing. Specifically, for global feature dis- tribution, we propose feature redistribution consistency learning strategy that mimics the prior distributions of the VFMs layer by layer. For local fine-grained details, to fur- ther enhance the specialized model's learning of fine-grained features from the foundation model, we design a localized representation transfer method, which promotes semantic alignment and generalization distillation between the spe- cialized model and the VFMs. TinyMGG framework offers the following advantages: i) Computational efficiency: clim- inates the need to incur the substantial computational over- head of foundation models during the inference phase; ii) Structural fiexibility: Seamlessly integrates various founda- tion models to enhance the performance of domain-specift models; iti Superior performance: achieving exceptional results with minimal parameters and faster inference speeds, as shown in Fig. The contributions of this work are summarized as follows:
strategies have
Single-source Domain Generalization (SDG). SDG meth- ods (Fan et al., 2021; Liu et al., 2022b) focus on extracting robust and invariant features solely from source data. Tra- ditional approaches include image transformations (Zhang et al., 2020), adversarial learning (Jing et al., 2023; Su et al., 2021b), model-driven augmentation (Yue et al., 2019), and feature- augmentation (Zhou et al., 2022a). To miti- gate overfitting risks caused by domain shifts, a variety of data augmentation strategies have been proposed (Xu et al., 2020; Huang et al., 2020; Zhou al., 2021). Adversarial techniques (Zhong et al., 2022; Chen et al., 2020; Qiao et al., 2020) leverage domain synthesizers to generate interpolated domains while maintaining semantic consistency through mutual information regularization.
We propose an efficient framework for transferring the generalization capabilities of large-scale Foundation Models to lightweight specialized models. This frame- work maintains robust SDG in medical imaging while requiring only minimal computational overhead.
Vision Foundation Model (VFM). Foundation models such as SAM (Kirillov al., 2023b), DINOV2 (Oquab al., 2023), CLIP (Radford et al., 2021), GIT (Wang et al., 2022), and Coca (Yu et al., 2022) represent notable breakthroughs. These large-scale foundation models, pre-trained on diverse datasets, have attracted considerable attention for their flex- ibility in adapting to a wide range of tasks (Devlin et al., 2018: 8; Touvron et al., 2023; Brown et al., 2020). Their re-
For global feature distribution, we propose a feature distribution consistency learning strategy that encour- ages the specialized model to hicrarchically mimic the prior distributions of the foundation model.
2

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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
markable generalization abilities have inspired extensive research into their potential for specialized applications, demonstrating their transformative influence across various domains (Ji et al., 2023; Ma & Wang, 2023; Wang et al., 2023a; Osco et al., 2023). This inspires us to consider how to fully leverage these properties during the training phase.
the probability parameter define a Bernoulli distribution, from which we draw À E RBxC Le., À Bern Q). To ensure a better imitation of global features, we randomly sample the augmented statistics os E I RBC from a uniform distribution and mix them with the feature distribu- tion of the foundation model. This distribution spans most feature statistics: o, ~ (O, 1), js ~ (O, 1). Then, the mixed statistics are computed as follows:
3. Method
Tmix = λo + (1 - (2 ), Bmix Aus+ (1 {))((?
The proposed MIG framework leverages VFMs such as SAM (Kirillov et al., 2023a). Dinov2 (Oquab et al., 2023), and SAMed (Ma et al., 2024) during the training phase to guide and enhance the single-domain medical image seg- mentation capabilities of lightweight models. The overall framework comprises two key components: Global Distri- bution Consistency Learning and Localized Representation Alignment. All strategies implemented during the train- ing phase, ensuring no additional computational overhead during the inference phase.
(2)
Finally, the mixed feature statistics is applied to mimic the global distribution of the foundation features X,
XSS
-L(2())
UFDM(X;) = Ymmxx
+ +mmix
(3)
(22))
Cross-Distribution Consistency Loss (CDCL). We intro- duce the LCDC loss, which enhances domain-invariant fea- ture extraction by aligning the soft predictions of the original and UFDM-modulas
samples. Specifically, we adopt the logit pairing method, where CDCL enforces consistency by aligning segmentation logit outputs before and after distri- bution modulation during training. Formally, minimize the bidirectional KI. divergence between the probabilistic distributions of the semantic predictions, fuufdm and y ensuring that the specialized model maintains distributional consistency under different styles:
3.1. Global Distribution Consistency Learning
Pre-training on large-scale datasets with diverse styles en- dows VFMs with superior generalization capabilities in global feature distribution, enabling them to adapt to wide variety of patterns. To leverage this advantage, we design the Universal Feature Distribution Modulation (UFDM), which adjusts the feature distribution of the lightweight model using the prior distribution of the foundation model. Ad- Consistency Loss (CCDC) to optimize the consistency of the lightweight model's 's feature distribution during the inference.
N-1
ditionally, propose the
Lene N
1
Σ
(CC
1=
(4)
(CC (patampper)))
Universal Feature Distribution Modulation (UFDM). We observe that the statistical properties of feature maps, such as mean and variance within deep network, serve as strong indicators for features with varying styles and content. In- spired by the instance normalization strategy in AdaIN (Huang & Belongie, 2017), we propose the UFDM module to modulate the feature distributions within the specialized model, as demonstrated in Fig. (a). Specifically, for a given intermediate feature X² and X:5 E RR "xxxxxx xO from the i-th block of the specialized and foundation models, H, W. and C' denote the height, width, and number of channels, respectively, and X,R, which is obtained by reprogramming the original foundation model features x. through the FRM module, as detailed in Section 3.4.
The Lene is designed to enhance the specialized model's ability capture high-quality feature representations, thereby improving its performance in domain-spectin tasks without relying on the foundation model during inference.
3.2. Localized Representation Alignment (LRA)
As shown in Fig. 3 (b), we design the Frequency- based Discriminative Content Enhancement and the Localized Representation Alignment Loss. These components work together to refine local feature representations, ensuring improved robustness in unseen domains.
AdaIN Ym, Bm)) (f (())//(()))))) Bm. (1)
Frequency-based Discriminative Content Enhancement (FDCE). To enhance domain-invarian and discriminative content in the frequency domain, we employ the Fourier Transform (FFT) to decompose feature representations from both the foundation and specialized models, followed by learnable filtering operations on amplitude and phase com- ponents. Specifically, given a pair of intermediate fea- ture representations 2,5 and reprogrammed features X,R
where (()), E BBC correspond to the spatially com- puted mean and standard deviation for each channel across the features f, 7' and fm can determine the direction of style transfer. We begin by sampling Q E IR BxC from a Beta distribution, Q ~ Beta(a, a), where a is empirically set to 0.1 (Zhou et al., 2021). The sampled Q is then used as
3

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MInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Training Stage
Vision Foundationn
Auxiliary Output
Patch Embxckling
Blla ILM
VIT Block
VIT Block
VIT Block
Decoder
Ground Truth
Image
Local View
Token
XN
Weight. Siwwing
Frequency- based Alignment
FRM
FRM
FRM
Global View
Distribution Consisiancy
LRA
UFDM
LRA
UFDM
LRA
UFDM
Inference Stage
Decodar
Cene
x
Output
Lightweight Specialized Model
seg
Figure 2. Illustration of the Proposed MIG Framework. UFDM is integrated into certain layers of the specialized model to generate style samples. LRA enhances content features both models in the frequency domain. The model further optimized with Lonc and LLRAA promoting the global and local transfer of generalized features to the specialized model. Segmentation loss constrains the outputs both original Cin UFDM-stylized features Cien, while foundation model Leey incorporated via co-training. During inference, only the low-cost specialized model is used, ensuring balance hetween generalization performance and efficiency.
RR XX*XC extracted from the i-th block of the specialized and foundation models via the FRM module, we first apply 2D FFT (Chi et al., 2020) obtain the corresponding frequency representations St and x,R As shown in the following equations, these frequency representations are then decomposed into amplitude and phase components:
inverse Fourier Transform (F-1), allowing the specialized model to leverage enriched frequency-domain represen- tations:
=F ([ (A:SP A.A
After applying spectral filters for content enhancement the spatial locations, we further propose phase spectrum- based Adaptive Channel Refiner to enhance content along the channel dimension. The key idea is that amplitude reflects style, whereas phase represents content. Thus, the phase component assumed to consistent between the foundation and specialized features X. and X; due its stable representation. Subsequently, channel weight CP and C₂ through applying SE blocks (Hu et al., 2018) to the phase element of the content enhanced features X.S and X.R.R as shown below:
(XRRA, 1.SA A([2)) 1,51)).
(5)
(X/P XSP ,(
(6)
To selectively enhance the inveriiatn and discrimi- native frequency components, we introduce two learnalle filters, WA and Wp (denoted as FDCE-A and FDCE-P), which are applied separately to the amplitude and phase spectra. The filtering operations are defined as follows:
obtain the refined
(R SSP SWpp @ [R SPP
(7)
C# SE(RR
SE(R,SP)
(10)
RRRR A.S. WW))
XX
SSSA
(8)
Then we product the channel attention with X and 2 to obtain the final refined feature map L.S and P.R.
where denotes element-win wise multiplication, and the hanced phase and amplitude representations are denoted as X.RP and X,RA,, respectively. The learnable filters are initialized with all elements set to one, ensuring that Sigmoid (W.) [0, 1]. Finally, the enhanced amplitude and phase spectra are reconstructed into feature maps using the
X. = Oer R
X = der (C + ss
(11)
where C denotes the Hadamard product, and der() (.) adapts the weights to align with the spatial dimensions of the fea- ture map, i.e., dee Rexix1 - Rexhxw

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TInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Original Statistics
Redistribution
SE
Mr
of
W
ex1
ex1
ex1
EXI
PET
HET
X W
x
h
ELRA
W
VFM
Random Channel-
Sample
((011)
FPT
2
W
(a) Universal Feature Distribution Modulation
(b) The proposed FDCE module
Figure 3. (a) The proposed Universal Feature Distribution Modulation (UFDM) module, by extracting feature distributions X and X from the foundation model specialized model, UFDM generates the redistributed samples. (b) Frequency- Discriminative Content Enhancement (FDCE) module utilizes Fourier transforms combined with learnable frequency-domatt filters WA and Wp enhance the discriminative content features.
Localized Representation Alignment Loss. We imple- ment the FDCE strategy across multiple feature scales to enhance feature representations different hierarchical lev- els. Specifically, we select set of intermediate feature maps from both the foundation model and the specialized model:
where As] are hyperparameters. A detailed discussion of loss term hyperparameters is in the Appendix.
4. Experiment
X = (2, i IR),
g = ( | Is}, (12)
To validate the effectiveness of our TinyMIG framework, we conduct extensive experiments on two medical image DG benchmark tasks. These include two medical segmentation tasks: the 2D joint optic disc (OD) and cup (OC) segmen- tation task (Almazroa al., 201 20; Chen al., 2023), as well 3D medical image segmentation task: the prostate MRI segmentation task (Chen et al., 2023). The Dice score metric (DSC) and Average Surface Distance (ASD) are uti- lized for evaluation on fundus and prostate task. We closely follow the implementation of (Hu et al., 2023) and use a U-shaped segmentation network with a modified ResNet- encoder as the specialized model and backbone for all competing approaches, ensuring fair comparison. Further details on the dataset and model architecture are provided in the appendix.
where I and Ig represent the selected sets of feature layers from the foundation and specialized models, respectively.
To enforce consistency between the feature representations, we introduce an L2-norm loss that minimizes the discrep- ancy between corresponding feature maps:
CLRA
A.R (c,h,, X (g, h w)
(13)
3.3. PEFT for Foundation Model
To enhance the adaptability of vision foundation models (VFMs) to the medical domain and improve feature align- ment with the specialized model, we adopt a two-step ap- proach. First, we employ the Adapter technique, as used DAPSAM (Wei et al., 2024b), which enables Parameter- Efficient Fine-Tuning each layer of the foundation model. Second, propose the Feature Reprogramming Module (FRM) to further refine the extracted features, making them more suitable for downstream tasks and specialized models. At its core, FRM utilizes residual blocks to perform feature dimension transformation and alignment, ensuring effective adaptation of foundation model representations.
4.1. Experimental Results
Experiment Setting. In experiments, we adopt the leave-one-domain-ou strategy commonly used in DG re- search. The model is trained on the single source domain and tested on the remaining K - unseen target domains. We compare with several recent state-of-the-art domain gen- eralization approaches, including: feature-space domain randomization Methods: MixStyle (Zhou et al.. 2021), TriD (Chen et al., 2023), and DSU (Li et al., 2022); Adversar- ial noise synthesis method: MaxStyle (Chen et al., 2022); Image augmentation methods for SDG in Medical Imag- ing: SLAug (Su et al., 2022b) and VPTTA (Chen et al., 2024). Advanced DG methods based on vision foundation models (e.g., fine-tuned VFs): Rein (Wei et al., 2024a) and DAPSAM (Wei et al., 2024b).
Training Objective. In this work, two distinct loss functions employed: auxiliary segmentation loss C. for the VFM and the segmentation loss E.eg for the specilized model. Long comprises combination of Dice loss and Cross-Entes Entropy loss. Thus, our overall optimization objective can be expressed by the following equation:
C Cre + A2 + >xg + ArCLRA + AsLoDC.
Comparison results: Tab. I and Tab. present the quan- titative comparison results of our TinyMIG and other DG
(14)
5

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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Table 1. Performance Comparison of our TinyMIG with SOTA methods Fundus segmentation task.
Optical Disc/ / Cup Segmentation (DSC f)
Optical Disc / Cup Segmentation (DSC 1)
Methods
D1
Ds
D4
Avg. t
D)
D3
D4
2%
Avg. 1
Optical
Disc
DSC
f
Optical Cup
DSC
1
ERM (Ronneberger et nl., 2015)
74.89
81.72
78.12
74.79
74.36
76.78
59.21
63.27
71,96
57.23
58.88
62.11
MixStyle (ICL (CCL2021)) (Zhou et al., 2021)
75.67
83.35
82.86
68 86
79.54
78.06
60.84
62.60
73.77
61.44
66.79
66.73
CSDO (TMI 2022) (Ouyang et 2022a)
78.40
82.02
81.46
75.51
81.09
79.70
65.11
70.79
76.19
65.26
65.28
68.53
MaxStyle (MICCA12022) (Chen el. 2022)
77.40
80.95
79.59
76.69
81.95
79.32
67.62
74.52
66.05
FFDM (CVPR 2022) (Zhang et al., 2022)
78.83
84,83
82.25
82.13
81.45
81.90
62.75
65,94
72.20
61.62
63.02
64.10
DSU (ICLR2022) (Li ct al. 2022)
76.88
82.17
81.12
82.36
83.09
81.12
61.26
70.16
74.10
63.19
59.65
65.67
SI.Aug (AAA12023) (Su et al., 20222)
79.83
83.42
83.18
81.17
83.57
82.23
64.53
71.30
75.94
64.52
67.12
68.28
TriD (MICCAT 2023) (Chen al, 2023)
78.35
82.19
83.62
80.18
81.65
81.12
66.67
70.85
74.13
67.53
66.96
69 29.23
MAD (CVPR2023)
al., 2023)
80.63
82.10
82.08
80.31
67.14
66.57
72.40
68.38
69:37
68.77
VPTTA (CVPR2002) (Chen et al. 2024)
79.41
84,86
80.01
62.01
80.85
77.43
68.41
73.86
69.01
51.01
69:85
66.43
DAPSAM (MICCA12024) (Wei al., 202-b)
85.36
89.01
85.45
87.29
90.38
88.27
76.38
76.80
17.14
72.62
72.01
74,99
SAMed (Nat. Commun2024) (Ma et al., 2024)
80.63
82.10
80.37
82.08
80.31
81.10
67.14
66.57
72.40
68.38
69:37
68.77
Ting-MIG(Ouuss
90.83
89.38
90.01
88,86
91.36
90.09
82,60
79.14
81.35
76,83
79.26
79.84
+5.47
+0.37
+4.56
+1.57
+0.98
+1.82
66.22
+2.34
+4.21
+4.21
+7.25
+4.85
Table 2. Performance Comparison of our MIG with SOTA methods on Prostate segmentation task.
Task
Prostate Segmentation (DSC
1)
Prostate Segmentation (ASD 1)
Single Seen Site
D2
D2
Pa
D4
Do
Do
Avg. f
D1
Da
Da
D4
D5
Do
Avg.
DSC
ASD 4
ERM (Ronneherger al., 2015)
71.81
65.56
43.9%
71.97
48.39
37.82
56.59
7.54
8.87
13.30
11.97
9.98
7.65
9.89
MixStyle (2hou et al., 2021)
73.24
58.06
44.75
66.78
49.81
49.75
57.06
4.98
5.77
6.30
5.21
5,98
6.26
3.73
CSDG (Ouyang et al, 2022b)
82.14
67.21
59.11
73.
67.38
73.23
70.37
3.51
4.08
4.56
3.58
4.46
4.17
4.06
CCSDG (Guo ct al., 2023)
80.62
69.52
65.
67.89
58.90
63.27
67.58
3.76
4.12
4.68
3.61
4.42
4.87
4.24
MaxStyle (Chen at. 2022)
81.25
70.27
62.09
58.18
70.04
67.77
68
3.40
4.32
3.67
4.12
3.77
ERDM (Zhang et 2022)
2022)
80.87
69.78
63.
65.39
69.84
67.15
69.37
3.45
3.82
4.35
3.37
3.89
4.03
3.82
S1.Aug (Sa et al., 2022h)
81.20
69.32
60,92
73.72
67.15
71.93
70.71
3.31
3.74
4.23
3.22
3.79
3.91
3.70
TriD (Chen ct 2023)
81,50
70.28
62,89
74.52
72.12
69.11
71.34
3,28
3.69
4.15
3.14
3.67
3.81
3.62
MAD (Qu et 2023)
80.87
69.78
63.16
65.39
69.84
67.15
69.37
3.49
3.81
4.33
3.36
3.87
4.01
3.82
VPTTA (Chen et al., 2024)
82.32
71.12
66,89
76.31
76.98
73.87
74.58
3.92
3.43
4.65
3.08
3.59
3.85
3.75
DAPSAM (Wei et al., 20244b
86.34
81.05
70.81
85.28
82.91
81.48
81.31
3.78
3.49
3.98
2.79
3.12
3.16
3.39
SAMed (Ma et al., 2024)
80.42
81,44
66,75
82.09
80.19
80.17
78.51
4.78
4.49
5.54
4.27
4.34
4.61
4.67
MIG (Ours)
65.82
84.52
76.45
86.61
83,70
81.53
83.09
3,81
3,30
3,52
1.62
3.09
3.02
3,06
-0.52
+3.47
+5.64
+1.33
0..79
+0.05
+1.79
-0.03
+0.19
+0.46
+1.17
-0.02
40.14
+0.33
DAPSAM) surpass specialized domain generalization meth- ods, demonstrating foundation models' superior capacity to address cross-domain shifts through enhanced feature rep- resentations. Our approach delivers consistent gains across metrics, surpassing DAPSAM (Wei et al., 2024b) (DSC: +1.78%, ASD: -0.59) and achieving remarkable 5.64% DSC improvement under Domain 3's challenging settings. Cru- cially, while maintaining computational efficiency (22.6 GFLOPs), our framework outperforms foundation model fine-tuning by eliminating the substantial inference over- head associated with foundation models, while achieving an optimal accuracy-efficiency tradeoff (Fig. 1). Fig. 4 presents segmentation results for two cases from unseen domains across different tasks. Our method accurately de- lineates organ structures and boundaries in images from unknown distributions, whereas other methods often con- fuse organ boundaries with surrounding tissues, leading to segmentation errors.
Smago
GT
14]
[d)
Figure 4. Comparisons across different SDG Methods: prostate imaging (The first two rows). fundus imaging (The last two rows) with ground truth (GT) and predictions (a-g). The subfigures (a) to
(g) correspond to: (a) TinyMIG, (b) DAPSAM (Wei et al., 2024b),
(c) TriD (Chen et al., 2023), (d) VPTTA (Chen et al., 2024), (c) MAD (Qu et al., 2023), (f) SLAug (Su et al., 2022b), (g) bascline.
methods on the fundus segmentation task and the prostate MRI segmentation task, respectively. As shown in Tab. 1, our method achieves the best segmentation performance on the fundus segmentation task, with average DSC score of 84.97% for OD/OC segmentation, surpassing the recent state-of-the-art DAPSAM by 3.92%, demonstrating the ef- fectiveness of our approach. Tab. compares prostate MRI segmentation performance across methodologies. Notably, foundation model-based fine-tuning approaches (e.g., Rein,
Various VFMs in TinyMIG. We select three VFMs: Med- SAM (base) (Ma et al., 2024), SAM (base) (Kirillov et al., 2023a), and DINOv2 (vit_ base) (Oquab et al., 2023). Tab. 3's last three rows show that all VFMs significantly boost segmentation accuracy on medical imaging tasks. MedSAM outperforms others, likely due to its medical image-specific
6

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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Table Comparison of different TinyMIG variants and fine- tuning-based methods in terms of segmentation performance (Dice 1). parameter count (M), throughout (liss,, and computational cost (G-FLOPs) during the testing phase. All experiments are con-
Table 5. Ablation study of different style variation methods in UFDM on prostate MRI task.
Mixstyle CrossNorm
DSU
Maxstyle
TInyMIG
ducted on an NVIDIA RTX 4090 GPU.
Dice
81.34
81.78
81.47
81.86
83.09
ASD 4 3.52
3.48
3.50
3.34
3.06
Methods
prostate
fundus
Params.
throughout FLOPs
DAPSAM
81.31
81,05
98.45M
43.91
58.61
Rein+SAM
79.85
79.36
95.78M
44.10
54.29
Effectiveness of CDCL. CDCL serves as the consistency
Rein+Dinov2
80.07
79.54
86.96M
45.72
50.72
MedSAM
78.51
75.92
93.74M
44.92
52.39
learning component in the Global Distribution Consistency Learning method, aiming to guide the model in learning style-invariant representations. As shown in the third row of Tab. 4, compared with only applying UFDM, CDCL signifi- cantly improves the segmentation performance by 4.3% in DSC. This demonstrates that the proposed logit pairing over KL divergence loss effectively enables the model to lever- age the generalization priors of foundational vision models, showcasing its superiority in learning domain-invariant rep- resentations.
TinyMIG+SAMed
83.09
84.68
22.52M
147.73
22.64
TinyMIG+SAM
82.69
83.57
22.52M
147.73
22.64
TinyMIG+Dinov2
82.53
83.05
22.52M
147.73
22.64
Table 4, Ablation experiments on each component in TinyMIG for the fundus and prostate tasks.
UFDM
Lode
FDCE
LLRA
Co-training
Fundus
Prostate
X
x
67.33
48.75
x
X
81.04
76.25
/
1
x
x
/
82.52
80.73
x
/
/
/
83.41
78.26
Effectiveness of FDCE. As shown in the fourth row of Tab. 4, the absence of FDCE for enhancing domain-invariant significant performance drop. We also visualize feature heatmaps, shown in Fig. 6, to further evaluate the effectiveness of the LRA module. In general, LRA in TinyMIG enables a more comprehensive focus on the organ structures and even edge details during segmentation. This is attributed to FDCE's spectral modula- tion in the frequency domain, which enhances the model's attention to details, edges, and richer texture information.
x
/
80.16
70.32
/
x
83.78
81.76
content features leads
84.97
83.09
pretraining. While SAM and DINOv2 are trained on nat- ural images, they still deliver strong segmentation results, demonstrating TinyMIG's robust generalization and feature transfer capabilities. Compared to DAPSAM, TinyMIG not only achieves superior segmentation performance on unseen domain samples but also offers significantly better inference efficiency: fewer model parameters (22.52 v.s. 98.45 M), faster inference speed (147.73 v.s. 43.92 fps), and reduced computational cost (22.64 v.s. 58.61 GFLOPs). These ad- vantages validate the adaptability of TinyMIG method to different VFMs. demonstrating its generalizability.
4.3. Further Evaluation
Comparison of different style variation methods with UFDM. In Tab. 5, we compare UFDM with MixStyle (Zhou et al., 2021), CrossNorm (Tang et al., 2021), DSU (Li et al., 2022), Maxstyle (Chen et al., 2022) and Trid (Chen et al., 2023). MixStyle and CrossNorm, being sim- ple linear combinations of known source domain samples, struggle to synthesize rare styles. When using MixStyle and CrossNorm, their segmentation performance is 81.34% and 81. 31.78, respectively. In contrast, our UFDM method effectively integrates diverse style features from the visual backbone model, significantly increasing the style diversity of generated samples, which in turn enhances the model's segmentation performance.
4.2. Ablation Studies of MIGy
We conduct ablation studies to evaluate the contribution of each component in our TInyMIG as summarized in Tab.
Effectiveness of UFDM. The UFDM module is the core of Global Distribution Consistency Learning. When using the UFDM module only, we directly supervise the stylized sam- ples with the cross entropy loss. As shown in the second row of Tab. 4, UFDM significantly improves model performance even without the proposed knowledge-driven consistency loss. This highlights the importance of diversifying train- ing samples. Meanwhile, we compare UFDM with other dyyelraadomizztionn- based DG methods, such as MixStyle (Zhou et al., 2021), DSU (Li et al., 2022) and Maxstyle (Chen et al., 2022), as illustrated in Tab. 5, UFDM achieves the best segmentation performance, further demonstrating the effectiveness of redistrbuting style features from vision foundation models.
A Ablation Analysis of FDCE. We conduct the experiments shown Tab. 6 investigate the impact of different mod- ules within the FDCE, The results indicate that removing any module negatively affects the segmentation performance on unseen domains, with FDCE-A having the most signif- icant impact. This suggests that the amplitude spectrum enhancement filter plays a crucial role in the FDCE module, likely because the amplitude spectrum is closely associated with style and texture information of extracted features. Ad-
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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Table Ablation study of LRA modules on prostate MRI task.
Table 8. Performance comparisons of different consistence lear- ing method on prostate task.
FDCE-P
FDCE-A
ACR
Dice f
ASD +
MSE
KL-div
JS-div
Ours
X
56.59
9,89
X
/
78.43
6.39
Dice
81.02
81.78
82.56
83.09
X
/
81.02
3.81
ASD
3.82
3.51
3.21
3.06
X 79.05
5,30
/
83.09
3,06
Tanger
Categury Labe
Souree
Dareis
Table 7. Comparison of Different Distillation Methods on prostate
Oyeizal Crp
Optiaal Disc
T
MRI task.
Dice 84.97
Dice 84.97
TinyMIG (Ours)
ChaKD
AttFFD
MLKD
Ours
Dice
81.02
81.78
80.56
83.09
ASD
3.82
3.51
4.03
3,06
ditionally, since the phase spectrum encodes more domain- invariant content features, the ACR module plays vital role in enhancing the content information.
Dice 20.
Dice 81.20
Comparison of Different LRA Methods. We compare LRA with other distillation methods at the feature and logit level, including the channel distillation ChaKD (Shu al., 2021), the attention-based distillation AttnKD (Komodakis & Zagoruyko, 2017) and logit distillation method MLKD (Jin et al., 2023). As shown in Tab. 7, our domain-invariiat feature alignment method achieves the best performance compared to other approaches. This stems from the ability of spectral dynamic filtering to facilitate domain-invariant feature transfer while effectively avoids interference from domain-specific features in LRA module.
MASsMM
The t-SNE visualization uses colors to distinguish cat- egories in the first column and domains in the second, with the magenta and violer dashed lines marking the distribution contours of two categories.
Figure 5.
Comparison of different alignment methods in CDCL. Tab. 8 presents the impact of various consistency losses, including KL-DDvergence (KL-Div), mean squared error (MSE), and Consen-Shannn sults indicate that the choice of consistency loss has minimal inffuence our method's 's overall performance, demonstrat- ing its robustness to different loss types.
Fig. 6, the LRA module improves semantic focus, as seen in second column, which highlights more accurate regions. In contrast, the third column not only overlooks some se- mantic categories but also attends to irrelevant background areas. This demonstrates that the LRA module effectively enhances content features related to semantic categories.
Divergence (JS-Div). The re-
Feature Discriminability Visualization. Fig. 5 presents t-SNE visualizations (Van der Maaten & Hinton, 2008) com- paring feature discriminability between TinyMIG and the DAPSAM method (Chen al., 2023). In the first column, our method effectively clusters samples from different do- mains within the same category, with well-defined distribu- tion contours outlined by magenta and violet dashed lines. In contrast, the DAPSAM method exhibits a noticeable gap between them. The second column shows that DAPSAM struggles to distinguish category features effectively, likely due to its limited adaptability to OOD conditions and large style variations. In comparison, TinyMIG demonstrates clear separation across domains and categories, highlighting its strong generalization capability to unseen domains.
5. Conclusion
We present TinyMIG, a novel framework that effectively transfers generalization from VFMs to compact architec- tures for medical imaging. TinyMIG introduces two syner- gistic components: Global Distribution Consistency Learn- ing, which establishes hierarchical feature alignment across domains through multi-scale distribution matching while preserving essential anatomical structures, and Localized Representation Aligmment, which enhances pathological de- tail capture through adaptive frequency filtering and discrim- inative feature refinement. This dual mechanism enables comprehensive learning of both global tissue organization and localized lesion patterns with remarkable computational efficiency. Extensive experiments with different VFMs val- idate that TinyMIG achieves superior performance while requiring significantly fewer computational resources than existing methods, establishing a new paradigm for develop- ing efficient specialized models medical image analysis.
Feature Visualization of LRA module. To investigate the impact of the LRA module on enhancing commin-invvarinn content features, we visualize the heatmaps of segmentation models with and without the LRA module. As shown in
8

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MInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
GT
Ours
W.O. LRA
DAPSAM
Baseline
Advances neural information processing systems, 33: 1877-1901, 2020.
Butoi, V. I., Ortiz, J. J. G., Ma, T., Sabuneu, M. R., Guttag, J., and Dalca, A. V. Universeg: Universal medical image segmentation. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 21438-21451, 2023.
Carlucci, F. M., D' I'Innccente, A., Bucci, S., Caputo, B., and Tommasi, T. Domain generalization by solving jig- saw puzzles. In 2019 IEEE/CVF Conference on Com- puter Vision and Puttern Recognition (CVPR), Jun 2019. doi: 11.1 10.11099/vp:22210000233 URL http: /ww. org 10..c.110/vvpppp 9022
Figure 6. Heatmap visualization of the features from the encoder of the specilized model enhanced by LRA on two medical samples.
Acknowledgments
This work was supported in part by the National Key Re- search and Development Program of China under Grant No.2021220140400 the Beijing Natural Science Founda- tion L222152, the National Natural Science Foundation of and National Natural Science Foundation of China under Grant No.62406347.
Chen, C., Qin, C., Qiu, H., Ouyang, C., Wang, S., Chen, L.. Tarroni, G., Bai, W., and Rueckert, D. Realistic ad- versarial data augmentation for mr image segmentation. Medical Image Computing and Computer Assisted Intervention 2020: 23rd International Confer- ence, Lima, Peru, October 4-8, 2020, Proceedings, Part 133, pp. 667-677 Springer, 2020.
China under Grant No.
Impact Statement
This paper presents work whose goal advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Chen, C., Li, Z., Ouyang, C., Sinclair, M., Bai, W., and Rueckert, D. Maxstyle: Adversarial style composition for robust medical image segmentation. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pp. 151-161. Springer, 2022.
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Tang, Z., Gao, Y., Zhu, Y., Zhang, Z,, Li, M., and Metaxas, D. N. Crossnorm and selfnorm for generalization under distribution shifts. In Proceedings of the IEEE/CVF In- teraaiionl Conference on Computer Vision. pp. 52-61, 2021.
Xu, Z., Liu, D., Yang, J., Raffel, C., and Niethammer, M. Robust and generalizable visual representation learning via random convolutions. International Conference on Learning Representations, 2020.
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Yoon, J. S., Oh, K., Shin, Y., Mazurowski, M. A., and Suk, H.-I. Domain generalization for medical image analysis: A survey. arXiv preprint arXiv:23 10.08598. 2023.
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TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
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TInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
SAM
DINOV2
ResNet34
0.55
8:35
Feature Sinilorit (Croimo)
0.10
8:38
Festore Similariy (Cedinn)
8:55
0.
Stotlarity
0.75
87
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6+
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8
sale2
xcaks
susict
Acale3
Kale4
>cak]
Mesure COS
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Figure 7. Feature Similarity Analysis of Backhone Models Across Different Architectures (SAM, DINOv2, and ResNet). We calculate the cosine similarity between features extracted different layers of the backhone networks for images from various domains in the fundus dataset. Scale refers to the first four VIT blocks of SAM (Kirillov et al., 2023a) and DINOv2 (Oquab et al., 2023).
The supplementary materials are organized as follows. In the section A, we analyze the motivation behind our proposed method. In the section B, we provide detailed description of the dataset and the data augmentation techniques used. Section C presents extensive additional experiments demonstrate the method's effectiveness.
A. Motivation and Generalization of the Foundation Model.
In this section, we investigate whether the encoded features of vision foundation models trained on large-scale visual image datasets exhibit properties beneficial for generalization. We select two vision foundation models: SAM-b and Dinov2 (ViT-B). alongside a specialized model, ResUnet34, evaluate their cosine similarity across different domain data on the fundus task. We examine the four-layer features from SAM and Dinov2 encoders: [Ag. X X3 x2 and the four-layer features from the Res ResUnetis Unet34 encoder: iXS, X X J. To assess the robustness of these features, data from domain, and domaing-s are input into the frozen SAM-b, Dinov2, and ResUnet34 encoders, producing the corresponding encoded features at each scale: R domains and S Sdomaing, where and j represent the different scale for VFMs (layer 3,6,9,12) and lightweight specialized models (layer 1,2,3,4), k depends different domains. Cosine similarities between the domain! features and features from other domains are then computed for each scale. The results, presented in Fig. 7, show that the cosine similarities of features from SAM and Dinov2 remain highly stable across different domains, with strong similarity observed even in the deeper scales. In contrast, the lightweight specialized model, ResNet34, demonstrates decreasing similarity as the network depth increases. From these findings, conclude that the features from the VFMs' encoders robust and distortion-invariint,, which is crucial for developing transferable models with strong generalization capabilities.
B. Datasets and Experimental Details
B.1. Datasets Details
To validate the effectiveness and versatility the proposed method, we conduct extensive experiments on three benchmark tasks: the joint Optic Disc (OD) and Cup (OC) segmentation task, the polyp segmentation task and the prostate segmentation task.
The OD/OC segmentation task comprises five public datasets collected from different medical centres, denoted as D (RIM-ONE-33 (Orlando et al., 2020)), D2 (EFFUGE(Almazee et al., 2018b)), D3 (ORIGA (Zhang et al., 2010)), D (REFUGE- SaAGGG--Vliaaaio/Teaaee (Almazroa et al., 2018b)), and D5 (Drishti-GS (Sivaswamy et al., 2014)). There are 159, 400, 650, 800, and 101 images from these datasets. We cropped a region of interest (ROI) centering at OD with size of 800x800 for each image following [19], and each ROI is further resized to 256 256 and normalized by min-max normalization. The
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MInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
DAPSAM
MIG
Rein SAM
Rein (Dinov?)
McISAM
Auy
TriD
Droe Pho)
Dioe
CSDG
DecpAll
Denasi
16-
3M
50M
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Figure 8. (a) The proposed TinyMIG significantly surpasses the SDG capabilities of SOTAs with extremely low computational cost and model parameter size. (b) Parameter analysis on the location UFDM. (c) Parameter analysis on the location of LRA.
Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD) are utilized for evaluation in this task.
The Prostate segmentation task comprises 116 MRI instances from six different clinical centers, aggregated from three public datasets, including NCI-ISBI13 (Bloch et al., 2015), I2CVB (Lemaître et al., 2015), and PROMISE12 (Litjens et al., 2014) datasets. Following the methodologies described (Liu et al., 2020a; 2022a), the dataset is preprocessed to standardize the field of view for the prostate region and resized to 384 x 384 in the axial plane. The assessment of prostate segmentation performance also employs the Dice Similarity Coefficient (DSC).
B.2. Experimental Details
We employ the AdamW optimizer (Loshchilov & Hutter, 2018) on all three medical image segmentation tasks, with (0.9, 0.999]. The initial learning rates set lo 0.0001. These rates decay according the polynomial rule 4 lo x (1 )0.9, where 4 denotes the learning rate at epoch 1, and T represents the total number of epochs, which are set to 200 for prostate segmentation and 100 for the joint segmentation of OD/OC and Polyp segmentation, with the batch size set as All our experiments are conducted on two 4090 GPU computing servers. Based on the experiments, we set (A as [L,1,0.5,0.500.5] empirically. In addtion, we select the 3-6-9-12 layers in foundation models and 0-1-2-3 layers in the specialized model, i.e., In (3, 6, 12), (0, 1, 2,3) in Eq. 12.
C. Extended Experiments.
Segmentation Performance and Inference Efficiency of TIny.IG on Unseen Domain Samples. As shown in Fig. 8 (a), thanks to the efficiency of the MIG framework, our method achieves significant advantages in both segmentation performance (Dice) and inference speed (fps) during the testing phase. The horizontal axis represents inference speed, and the vertical axis represents segmentation performance on unseen domain samples in the prostate task. Compared to the state-of-the-an art methods DPSAM, our TinyMIG not only achieves superior segmentation performance on unseen domain samples but also offers significantly better inference efficiency: fewer model parameters (22.52M 98.45M), faster inference speed (147.74 vs. 43.91).
Location of LRA. Fig. 8 (c) examines the impact of inserting the LRA module at different network layers on performance. Similar to the positions investigated for UFDM, we evaluate the effect of varying insertion points. The results show that skipping any layer results a slight performance drop. This demonstrates that the LRA module effectively filters domain- specific features and enhances ionain-invariant features, enabling its integration at all layers to improve the capability of generalizable representation transferring of the VFM models.
Location of UFDM. We investigated the impact of UFDM insertion at different positions, as shown in Fig. 8 (b). LO represents UFDM insertion after the first Conv-BN-ReLLJ layer (Layer 0), while LI and I.3 correspond to insertion after the first two and all ResNet layers, respectively. LI yields the best performance, with a slight decline in LO and L2. However, we observed a significant performance drop when UFDM was inserted at L.. This may be due to the fact that shallow layers in deep neural networks capture more style-related channel statistics, whereas deeper layers tend to encode more semantic information.
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TInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Table 9. Experiments on the Fundus dataset different loss
A
A2
A
Aa
Às
Fundus Dataset
1.0
1.0
1.0
1.0
1.0
83.18
1.0
1.0
1.0
1.0
0.5
83.66
1.0
1.0
1.0
83.72
1.0
1.0
0.5
1.0
1.0
83.29
1.0
1.0
1.0
1.0
2.0
82.96
1.0
1.0
1.0
2.0
1.0
82.30
1.0
1.0
82.37
1.0
0.5
0.5
0.5
0.5
84.12
0.5
1.0
0.5
0.5
0.5
84.27
1.0
1.0
0.5
0.5
0.5
84.97
Image
GT
(8)
(b)
(c)
(d)
(f)
(g)
Figure Comparisons across different SDG Methods on fundus imaging with ground truth (GT) and predictions (a-g). The subfigures (a) to (g) correspond to: (a) TinyMIG, (b) DAPSAM (Wei et al., 2024b), (c) TriD (Chen et al., 2023), (d) VPTTA (Chen et al.. 2024), (e)
MAD (Qu et al., 2023), (f) SLAug (Su et al., 2022b), (g) baseline.
The impact of hyperparameters for loss terms. We also present the results for detailed weight settings for loss terms to Tab. 9. Based on these results, our TInyMIG method not particularly sensitive to the hyperparameters for loss terms. In this work, we select set of hyperparameters [Aı, A2, λ3, 2a, As] [1.0, 1.0, 0.5, 0.5, 0.5] that exhibit the best performance the default settings.
More Qualitative results. As shown in Fig. and Fig. 10, present additional visual results on the fundus and prostate tasks. Visual comparisons reveal striking advantages: TinyMIG precisely captures anatomical boundaries in unseen domains, whereas competing methods exhibit tissue confusion and boundary leakage artifacts, particularly in low-contrast regions. This demonstrates our method's 's superior generalization to distribution shifts.
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TInyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Image
GT
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure Comparisons across different SDG Methods on prostate imaging with ground truth (GT) and predictions (a-g). The subfigures (a) (g) correspond to: (a) TInyMIG, (b) DAPSAM (Wei et al., 2024b), (c) TriD (Chen al., 2023), (d) VPTTA (Chen et al., 2024), (e)
MAD (Qu et al., 2023), (f) SLAug (Su et al., 2022b), (g) baseline.
17


Reviews:
[{"review_id": "Z26Z58wWZl", "reviewer": "Reviewer_mFeP", "summary": "The authors propose a method to efficiently make use of generalizable features of visual foundation models by distilling learned distributions to a smaller, more efficient model. Thereby, they propose four main components: \n\n1. Global Distribution Consistency Learning: Forcing the “specialized model” (i.e. student model) to be less sensitive towards changes in distribution by augmenting the feature statistics (i.e. mean and sd) of the student across different layers with the statistics of the foundation models layers.\n2. Cross-Distribution Consistency Loss: Forcing the segmentations of the student with and without the augmented features to be closer. \n3. Localized Representation Alignment: Forcing the features of the transformed foundation models features and the student’s features to be close. \n4. Frequency-based Discriminative Content Enhancement: Enforcing phase and amplitude of the FFT transformed student and teacher features to be close. Thereby preserving semantics (phase) and increasing robustness towards style shifts (amplitude)\n\nThe authors show superiority of their methods compared to a variety of different methods.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer mFeP,\nWe sincerely appreciate your comprehensive review and highly constructive feedback on our manuscript. Below we provide point-by-point responses to address all of your concerns:\n\n**1.Some Typos**:\n\n*(1) Revised: Comparative Results of Perturbation Methods vs. UFDM (Lines 368-371)*: \n\nWe sincerely apologize for this oversight. The comparative analysis of perturbation methods was indeed detailed in Table 5 and Section 4.3. We have now updated the implementations of DSU and MaxStyle as shown in the revised table.\n\n||Mixstyle|CrossNorm|TriD|DSU|Maxstyle|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Dice↑| 81.34| 81.78| 82.03|81.47|81.86| **83.09**|\n|ASD↓|3.52|3.48|3.27|3.50|3.34|**3.06**|\n\nNotably, UFDM achieves superior performance by leveraging the style prior of foundation models. We will carefully finalize Table 5 and correct the citation error (Lines 368-371).\n\n*(2) The ASD of OC/OD:*\n\nWe sincerely apologize for this oversight. The experiments were indeed conducted but inadvertently omitted from the appendix. The experimental results are shown below:\n\n|Metric|ERM|MixStyle|CSDG|MaxStyle|EFDM|DSU|SLAug|TriD|MAD|VPTTA|DAPSAM|SAMed|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|**OD Avg↓**|12.90|11.75|11.52|11.16|10.24|10.33|10.46|9.84|9.46|9.46|12.75|10.68|**5.66**|\n|**OC Avg↓**|16.59|13.09|12.58|11.92|10.76|11.28|11.36|10.29|10.28|10.28|13.48|11.58|**6.03**|\n\n(3) We correct this lapse. The corrected data for Table 8 is provided below. As shown, only the MSE loss significantly impacts the results, while other consistency losses show limited effects.\n\n|Methods|MSE|KL-div|JS-div|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|\n|Dice↑|81.21|82.73|82.47|**83.09**|\n|ASD↓|3.79|3.15|3.23|**3.06**|\n\n**2.The Description of Baseline, FRM and Specialized Models**:\n\n(1) **Baseline**: We sincerely apologize for any confusion caused. As detailed in Section four (Lines 244-245), we adopt ResUnet34 as the specialized model for fair comparison with other SDG methods. For the baseline, we adopted a ResNet34-UNet model without additional domain generalization components.\nWe will clarify this further to improve readability and more willing to make all the code fully open-source to facilitate reproducibility and contribute to the advancement of the community.\n\n(2) **FRM**: We sincerely appreciate your thorough review. As noted in Lines 269-271, the FRM's primary role is to transform foundation model features through a simple convolutional block (conv-BN-ReLU) for channel/resolution alignment with the pyramidal features of specialized model (Unet with a Resnet34 backbone), enabling Localized Representation Alignment. While essential, this component is not our core technical contribution, which explains its brief treatment. We are willing to expand the technical details in revision and open-source all code to ensure reproducibility.\n\n**3. Additional Dataset Descriptions and Evaluation Metrics**: \n\n(1) **Prostate dataset**: The prostate dataset details are provided below, consistent with prior SDG works (e.g., DAPSAM, CSDG). Post identical preprocessing, the slice counts per domain are tabulated. We sincerely appreciate your feedback. Additional details can be found in Appendix B.1.\n||D1|D2|D3|D4|D5|D6|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Cases|30|30|19|13|12|12|\n|Slices|381|354|449|162|249|145|\n\n(2) **HD metric**: We sincerely appreciate your suggestion regarding the Hausdorff Distance (HD), a critical metric for segmentation evaluation. As shown in the comparative results below, our method achieves the best HD performance on the prostate dataset, demonstrating its effectiveness. We would be delighted to include this evaluation metric in our revisions. \n\n|Method|ERM|MixStyle|CSDG|MaxStyle|EFDM|DSU|SLAug|TriD|MAD|VPTTA|DAPSAM|SAMed|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|HD↓|73.51|65.74|47.22|54.91|53.71|48.31|45.91|45.72|46.12|41.06|35.76|43.89|**30.03**|\n\n(3) **standard deviations**: Yes, we are very happy to include standard deviations on prostate dataset to ensure a fairer comparison of all methods, as shown in the table below. We would be pleased to incorporate this addition in the revised manuscript. We sincerely appreciate your suggestion. \n|Method|ERM|MixStyle|CSDG|MaxStyle|EFDM|DSU|SLAug|TriD|MAD|VPTTA|DAPSAM|SAMed|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|DSC↑|56.59±8.79|57.06±6.32|70.37±5.61|67.58±5.63|68.27±5.91|69.37±3.96|70.71±4.22|71.74±6.59|69.37±2.89|74.58±3.77|81.31±1.28|78.51±4.36|**83.09±0.57**|\n\nI sincerely apologize for any minor errors caused by my limited writing skills, which may have inconvenienced your review. If anything remains unclear, please do not hesitate to point it out—I will carefully consider and refine it. Once again, I truly appreciate your valuable suggestions for improving the quality of the paper!\n**Finally, We will implement these modifications in the revision and open-source all the code/weights for reproducibility.**", "claims_and_evidence": "The evidence is mostly clear and convincing with the exception for line 368 to 371, where the authors refer to a result that cannot be seen in the figure. Furthermore, there are no results for the ASD for the OC/OD segmentation task. Finally, Table 7 and 8 seem like the results are not carefully pasted, as the first two columns of both tables are the same to the second digit.", "methods_and_evaluation_criteria": "The authors compare their method against several relevant domain generalization methods- However, comparing their method against the base architecture of their “specialized model” as well as a fine-tuned version of the used foundational model is desirable to clearly see the effect of their method. (In some figures, the authors use a “baseline” method, but since there is no reference to the method in the paper, it is unclear what is being shown there.)\n\nThe datasets chosen in the paper are appropriate for providing evidence for the authors claims. However, a more detailed overview of the prostate dataset is desirable (i.e. number of images per center)\n\nThe chosen metrics to compare the different methods are appropriate, however, the authors should consider adding Hausdorff distance for completeness. \n\nIn addition to the mean segmentation metrics, standard deviations would be also helpful to assess the significance of differences in mean.", "theoretical_claims": "There are no theoretical proofs in the paper.", "experimental_designs_or_analyses": "The proposed modules and corresponding losses seem to be sensible and mathematically sound, except for a typo in γmix where is should be σs  and μs instead of σ(s) and μ(s).\n\nOne of the vital parts of the architecture design, the “FRM” block, is not described. Instead, the authors refer to a non-existing section. Furthermore, the authors refer to the appendix for further information regarding the network architecture of the specialized model, which also cannot be found in the appendix. This makes the paper non-reproducible and opaque.\n\nDespite claiming to use Dice and ASD for evaluation of both datasets, the authors fail to provide the ASD results for the OD/OC segmentation task. \n\nThe authors claim superiority of their UFDM compared to other style randomization-based DG methods and thereby refering to figure 3. However, no such evidence can be seen in figure 3 (which is a description of the method) nor in any other figure in the paper.", "relation_to_broader_scientific_literature": "The paper is in line with previous findings that foundational models generalize well [1]. The authors show that the specialized model performs well despite being less complex than the teacher (foundation) model. This is coherent with literature as well. [2] The authors combine methods from the literature with their own ideas. (e.g. DAPSAM [3], Squeeze and Excitation [4] and  AdaIN [5] with their FDCE module.)\n\n[1] Oquab, M., Darcet, T., Moutakanni, T., et al. (2024). DINOv2: Learning Robust Visual Features without Supervision. arXiv preprint arXiv:2304.07193. https://arxiv.org/abs/2304.07193.\n\n[2] Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531. https://arxiv.org/abs/1503.02531.\n\n[3] Wei, Z., Dong, W., Zhou, P., et al. (2024). Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation. arXiv preprint arXiv:2409.12522. https://arxiv.org/abs/2409.12522.\n\n[4] Hu, J., Shen, L., Albanie, S., et al. (2019). Squeeze-and-Excitation Networks. arXiv preprint arXiv:1709.01507. https://arxiv.org/abs/1709.01507.\n\n[5] Huang, X., & Belongie, S. (2017). Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. arXiv preprint arXiv:1703.06868. https://arxiv.org/abs/1703.06868.", "essential_references_not_discussed": "The authors included all citations necessary to understand their work and according to my knowledge are not missing any important references.", "other_strengths_and_weaknesses": "The paper lacks clarity and reproducibility (e.g. missing description of baseline, unclear method “ERM” in tables, missing description of a vital part of the architecture (FRA)). \n\nFurthermore, in the paper are several mistakes such as missing contend that was referred to, multiple references that are two times in the reference sections, wrong order of the legends in Figure 5, allegedly wrongly pasted results in the first two columns of Table 7/8.", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "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": "FdVCiwktRR", "reviewer": "Reviewer_uvPv", "summary": "The authors propose a transfer generalization framework, TinyMIG, to address the issue of single-domain generalization caused by the diversity of imaging devices and variabilities among data collection centers. To capture both global feature distribution and local fine-grained details, they developed a global distribution consistency learning strategy and a localized representation alignment method to mimic the prior distribution of the foundation model, promoting semantic alignment and generalization distillation. They validated their model on two domain generalization segmentation benchmarks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer uvPv,\n\n**R-W1 KL for Domain-invariant Features**\n\nWe sincerely appreciate your insightful concerns. The bidirectional KL divergence serves two key purposes in our approach: \n\n(1) It ensures stable and consistent semantic outputs before and after feature perturbation. This is because the perturbed features share identical semantic content with the original features while exhibiting different styles. Thus, the bidirectional KL divergence emphasizes invariant pixel-level semantic information across styles, enhancing model stability and reducing sensitivity to style variations. \n\n(2) As you rightly pointed out, global semantic alignment alone is insufficient. \nOur additional local representation alignment directly matches intermediate-layer features at a fine-grained level, further facilitating domain-invariant feature learning. These two components work synergistically to enable the generalization transferring from foundation model to specialized model.\n\nAlso, we are glad to refine the description regarding 'KL-divergence-based domain-invariant feature learning' to adopt a more measured tone.\n\n**R-W2 Motivation for Frequency-Domain Alignment and how it works**\n\n*(1) Motivation for Frequency-Domain Alignment:*\n\nOur approach is motivated by several key observations:\n\n(a) In visual domain generalization, a critical research direction involves learning domain-invariant content representations from frequency-space features. Prior studies [r1,r2] have well-established that phase components predominantly encode domain-invariant content, while amplitude components capture domain-specific style properties.\n\n(b) As shown in Appendix Fig. 7, foundation models exhibit more generalized features, demonstrating stable responses across diverse domains. We thus leverage their intermediate features as teacher guidance to optimize our amplitude and phase filters more effectively, enhancing the student model’s imitation of domain-invariant representations from the foundation model.\n\n[r1] Wang, Kunyu, et al. \"Generalized uav object detection via frequency domain disentanglement.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[r2] Xu, Qinwei, et al. \"A fourier-based framework for domain generalization.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n\n*(2) How to ensure the robustness:*\n\n(a) We first decouple spatial features into amplitude spectra (style-related) and phase spectra (content-related) via Fourier transform. An amplitude filter suppresses domain-specific amplitude variations, while a phase filter reinforces domain-invariant phase features. Both filters are optimized via Eq. (13)’s local alignment loss, encouraging the student model to mimic the teacher’s generalized frequency-domain characteristics.\n\n(b) As evidenced in Table 4, our Frequency-Domain Consistency Enhancement (FDCE) significantly improves generalization capcity. Moreover, the heatmap visualizations in Fig. 6 (columns 2 vs. 3) demonstrate that without Localized Representation Alignment (LRA), the model’s attention is distracted by complex and stylic backgrounds, whereas FDCE enables precise focus on target organs in unseen domains.\n\n**R-W3 More experiments on brain anatomical datasets**\n\nWe sincerely appreciate your valuable suggestion regarding expanding the experimental datasets.\nWe have already made several efforts in this direction:\n\n(1) Comprehensive Metrics: To thoroughly validate our method's effectiveness, we employed three evaluation metrics - DSC, ASD, and HD (as detailed in our response to Reviewer #1).\n\n(2) Statistical Rigor: Beyond average metrics, we additionally incorporated standard deviation measurements to assess our method's performance stability.\n\n(3) In direct response to your insightful suggestion, we have now supplemented our evaluation with an additional polyp segmentation benchmark [r3] (We sincerely apologize that this differs from your suggested brain dataset - we will carefully consider your recommendation for future work). This new evaluation consists of four public datasets from different medical centers:D1 (CVC-ClinicDB): 380 images, D2 (CVC-ColonDB): 612 images, D3 (ETIS): 196 images and D4 (Kvasir): 1000 images.\nAs shown in the following table, our method achieves sota performance on this challenging benchmark, further demonstrating its effectiveness. We are deeply grateful for your thoughtful comments and will incorporate all these improvements in our revised manuscript.\n\n|Methods|ERM|MixStyle|CSDG|MaxStyle|EFDM|SLAug|TriD|MAD|VPTTA|DAPSAM|SAMed|**Ours**|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|DSC↑|54.82|66.50|67.60|67.60|62.62|68.01|67.20|71.17|72.83|74.74|71.93|**77.92**|\n\n[r3] Chen, Ziyang, et al. \"Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.", "claims_and_evidence": "Yes, the claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria are well-suited to the problem, with relevant benchmark datasets and effective metrics for assessing the network's performance.", "theoretical_claims": "I believe the paper is more focused on practical applications.", "experimental_designs_or_analyses": "I reviewed the complete experimental design and analysis section, including the performance comparison of TinyMIG with SOTAs, ablation studies, and t-SNE/Heatmap visualizations.", "relation_to_broader_scientific_literature": "The contributions are very much related to the broader scientific literature, can be integrated with any vision foundation models, in terms of medical image segmentation tasks.", "essential_references_not_discussed": "The authors covered and discussed SOTA literature related to the domain.", "other_strengths_and_weaknesses": "**Strengths**\n\n*S1.* The paper is well-written, with clear pictorial depictions and a solid methodological development.\n\n*S2.* The integration of instance normalization (AdaIN) within the global distribution consistency learning module is unique.\n\n*S3.* The authors conducted extensive ablation studies and cross-validation, validating the effectiveness of various network parameters, loss functions, and hyperparameters.\n\n*S4.* The problem is well-motivated, and the experiments are comprehensive, covering two domain generalization benchmarks and comparing their performance with SOTA models.\n\n\n**Weaknesses**\n\n*W1. Concern about domain-invariant features.* I am unsure how minimizing the bidirectional KL divergence between the probabilistic distributions of the semantic predictions would encourage the predictor to capture domain-invariant features. While I agree that this strategy would better align the predictions across two domains, the underlying feature representations might still contain domain-specific biases that are not addressed by the loss in Eq. (4). Furthermore, KL divergence does not directly ensure domain-invariant features, as it only aligns soft predictions (according to the equations), not the underlying representations. Additionally, the model could overfit to the modulated distribution, and relying on soft predictions alone may not capture meaningful domain invariance, leaving domain-specific biases unaddressed in the feature representations.\n\n*W2. Motivation for frequency-based alignment.* I found the purpose and motivation for developing an alignment loss based on frequency-based discriminative content enhancement unclear. How do they ensure improved robustness in unseen domains?\n\n*W3. Experimentation on complex anatomical segmentation datasets.* While the current experimentation is comprehensive, I would like to see additional experiments on brain anatomical segmentations using diverse datasets (e.g., OASIS, ABIDE, ADNI, etc.). This would further strengthen the validity of the proposed approach and its applicability to a wider range of medical imaging data.*\n\n**Minor Concerns.**\n\n- FDCA module/L_LRA is missing in the figure, although it is mentioned in the caption.\n- The feature reprogramming block needs further explanation and thorough validation. How is it effective in improving network. performance?", "other_comments_or_suggestions": "The current rating reflects the generalizability of their proposed approach with any vision foundation model, solid experimentation, and ablation studies, paving the way for improved domain generalization tasks compared to SOTA models. I strongly recommend the authors to consider the raised weaknesses and concerns and address them in the rebuttal.", "questions_for_authors": "Please see the weaknesses.", "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": "1gaMolfXqb", "reviewer": "Reviewer_3XUJ", "summary": "This paper proposes to address the challenge of single-source domain generalization in the field of medical imaging. The authors propose to leverage the generalization knowledge embedded in current vision foundation models to enhance the generalization capabilities of smaller-capacity segmentation models. They enables the smaller segmentation models to learn generalization knowledge through both global and local learning during the training phase, thereby eliminating the need for additional inference with the vision foundation models. Experiments are conducted on two datasets to validate the effectiveness.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 3XUJ,\n\nWe sincerely appreciate your comprehensive review and highly constructive feedback on our manuscript. Below we provide point-by-point responses to address all of your concerns:\n\n1. **phase component attention mechanism**\nWe sincerely appreciate your feedback. In the field of domain generalization, the magnitude spectrum and phase spectrum are typically regarded as representations of style and content information. Since the phase spectrum exhibits more stable and generalizable content characteristics, we employ phase-spectrum-based attention to align and enhance content features between the foundation model and the specialized model. This strategy further mitigates interference from domain-specific style information, thereby facilitating more effective transfer of generalizable features.\n\n2. **more experiments about vary sizes of small models**\nWe sincerely appreciate your valuable suggestion regarding model improvement. As recommended, we have conducted additional experiments with different specialized model sizes (ResUNet-18) to further validate our approach. The comprehensive results are presented in Table [X] below, which demonstrates consistent performance across varying model capacities.\n\n|Task|**S-model**|#Param|Fundus Seg. (DSC)|Prostate Seg. (DSC)|\n|:-:|:-:|:-:|:-:|:-:|\n|Prostate|Unet(ResNet-18)| 11.93 M | 79.26| 81.83|\n|Prostate|Unet(ResNet-34)| 22.54 M | 84.97| 83.09|\n\n3. **Localized Representation Alignment Loss**\nWe sincerely appreciate your insightful comment. As demonstrated in Tab. 7 of our comparative experiments examining different LRA implementations, our proposed domain-invariant feature alignment method consistently outperforms other knowledge distillation approaches. This performance advantage primarily derives from our spectral dynamic filtering mechanism, which achieves two critical objectives: (1) enabling effective cross-domain generalizable feature transfer while (2) actively suppressing domain-specific feature interference within the specialized model.\n\n\n4. **Necessity of the FDCE-P Module**\n\nWe thank the reviewer for their careful evaluation.\n\n(a) The necessity of the FDCE-P Module is motivated by several key observations: In visual domain generalization, learning domain-invariant representations from frequency features is crucial. Prior works confirm that the phase component encodes content features (domain-invariant), while the amplitude component captures style features (domain-specific). Accordingly, we propose dual spectral filters to separately process these components, precisely eliminating style artifacts while preserving content features. This design significantly enhances the transfer of generalizable representations from vision foundation models.\n\n(b) Furthermore, Our quantitative results in Table 6 empirically demonstrate the necessity of the learnable phase component filter (FDCE-P). The synergistic operation of both magnitude and phase spectral filters enables effective transfer of generalization capabilities from visual foundation models to specialized models. \n\n5. **The noise mixup of UFDM**\n\nWe sincerely appreciate your insightful suggestions. We adopt random mixup between foundation model features and noise for two reasons:\n\n(1) Vision foundation models learn image representations from large-scale natural image datasets, which encompass diverse styles during training. This rich stylistic variability enhances the generalization capability of specialized model when applied to unseen target domains with novel styles.\n\n(2) Mixup (vs. full features) enhances the stylistic diversity of perturbed features by simultaneously incorporating partial style priors from the foundation model and randomized noise components, thereby facilitating the specialized model’s learning of domain-invariant features. The introduction of random noise compels the model to maintain semantic consistency across a broader spectrum of stylistic variations, effectively promoting the imitation of the base model’s globally generalized features.", "claims_and_evidence": "1. The rationale behind introducing a phase component attention mechanism after the learnable filters in the local alignment module needs further elaboration to better understand its contribution to domain-invariant feature enhancement.\n2. Experiments with varying sizes of small models, such as a ResNet-18-based UNet, are needed to fully demonstrate the generalizability of the TinyMIG framework.", "methods_and_evaluation_criteria": "The methods and evaluation criteria are aligned with the problem of single-source domain generalization in medical imaging. The framework effectively leverages knowledge distillation from foundation models, and the usage of benchmark datasets such as fundus and prostate imaging provides a solid basis for evaluating segmentation performance and generalization capabilities.", "theoretical_claims": "The use of AdaIN-based methods to generate mixed statistical parameters is widely recognized and accepted within the domain generalization community, lending credibility to the proposed approach.", "experimental_designs_or_analyses": "* Can the Localized Representation Alignment Loss be replaced with other forms of constraint functions? \n* In the FDCE module, is the learnable filter for the phase component necessary? To the best of my knowledge, phase components are generally stable. Why is there a need to filter \"domain-varying features\" in this context?", "relation_to_broader_scientific_literature": "This approach is reminiscent of techniques like AdaIN for style transfer and frequency-domain methods for domain adaptation, but it uniquely combines these ideas to address the specific challenges of medical imaging.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "# Strengths\n* The paper is well-structured, with clear\n* It achieves good results on fundus and prostate datasets, improving segmentation performance while maintaining low inference speed and parameter count. \n* The release of code helps in this field, promoting broader adoption and research.\n\n# Weaknesses\n* The integration of Gaussian noise with statistical feature parameters in the UFDM module, rather than directly replace the smaller model's parameters, requires a more detailed explanation to elucidate its role in enhancing generalization capabilities. \n* The local alignment module's phase component attention mechanism, applied after two learnable filters, needs a clearer justification regarding its interaction with domain-invariant features and its overall contribution to the module's functionality. \n* While the role of different foundation models within TinyMIG has been explored, additional experiments with varying sizes of small models, such as a ResNet-18-based UNet, are also necessary.", "other_comments_or_suggestions": "None", "questions_for_authors": "The rationale behind introducing a phase component attention mechanism after the learnable filters in the local alignment module needs further elaboration.", "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": {"ethical_review_concerns": "None"}}, {"review_id": "VfCtC9MnHa", "reviewer": "Reviewer_peNL", "summary": "This paper addresses the challenge of single-source domain generalization in medical imaging by proposing a novel framework that transfers generalization knowledge from large vision foundation models to specialized smaller models, thereby eliminating the inference burden of large models during the testing phase. \nSpecifically, the authors introduce a dual-level knowledge transfer approach, encompassing both global and local perspectives. The proposed method demonstrates superior segmentation performance and inference efficiency across two medical domain generalization tasks.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer peNL:\n\n**1. discuss the novelties with some strongly related works**\n\nWe sincerely appreciate your valuable feedback. Below, we clarify the key distinctions and advantages of our method compared to the cited works from two perspectives: motivation and implementation. These points will be thoroughly addressed in our revised manuscript.\n\n*(1) Motivation:* The cited methods essentially perform fine-tuning by directly adjusting intermediate features of large foundation models in the frequency domain. In contrast, our approach transfers generalizable knowledge from foundation models to specialized models via frequency-domain transformation. Crucially, our method eliminates the need for foundation model inference during testing, making it significantly more efficient than fine-tuning-based approaches.\n\n*(2) Implementation:* Our frequency-domain content enhancement module requires only two lightweight filters to achieve generalized feature transfer. This design is notably simpler than existing solutions, which rely on additional parametric network layers or computationally intensive attention mechanisms.\n\n**2. The Issue of UFDM module**\n\nWe sincerely appreciate your constructive suggestions. We are pleased to provide further clarification about the UFDM module and will incorporate these details in our revised manuscript. Below, we elaborate on both the implementation and motivation:\n\n(1) Implementation:\nThe UFDM (Universal Feature Distribution Modulation) employs Adaptive Instance Normalization (ADAIN) to perturb the primary channel statistics of the specialized model by leveraging the style priors from large foundation models. This design enables effective style transfer while maintaining the model's core functionality.\n\n(2) Motivation:\nVision foundation models acquire comprehensive image representations from large-scale natural image datasets, which inherently capture diverse stylistic variations during training. By exploiting these rich style priors through our UFDM module, we significantly enhance the generalization capacity of oriented object detectors when encountering novel domains with previously unseen styles.\n\n(3) Experimental Validation:\nAs demonstrated in Figure 5, our UFDM module substantially outperforms existing feature perturbation approaches across all evaluation metrics. These compelling results validate the superiority and effectiveness of our proposed method.\n\n**3. The rationale behind mixing the statistical feature**\n\nBuilding upon this framework, we innovatively adapt the mixup technique from image augmentation to effectively blend the channel-wise prior distributions between the specialized model and the vision foundation model. This strategic implementation serves two key purposes:\n\n(1) It generates samples with significantly enhanced stylistic diversity, and\n\n(2)Through our proposed consistent bidirectional KL divergence loss, it robustly facilitates the specialized model's ability to learn domain-invariant features across these diversified style variations.\n\n**4.Experimental Analysis of UFDM/LRA Module Location and Hyperparameter α**\n\n(1) We sincerely apologize for placing the experimental results regarding UFDM and LRA module positioning in the supplementary materials (Figure 8(b)-(c)). The findings reveal several important insights: Figure 8(b) demonstrates that bypassing any network layer leads to measurable performance degradation, confirming that the LRA module successfully suppresses domain-specific features while effectively preserving domain-invariant characteristics. Furthermore, Figure 8(c) indicates optimal performance is achieved at the L1 position, with marginally reduced effectiveness at L0 and L2. Notably, we observed substantial performance deterioration when implementing UFDM at the L3 level. This phenomenon likely stems from the fundamental property of deep neural networks where shallow layers predominantly capture style-related channel statistics, while deeper layers primarily encode high-level semantic information.\n\n(2) We sincerely apologize for any confusion regarding the ablation studies of parameter α. Our selection of α=0.1 was informed by the established MixStyle methodology, a canonical approach for statistical parameter mixup. The parameter α serves as the shape parameter of the beta distribution governing the convex weight λ in Equation 2, where smaller α values skew λ toward extreme values (0 or 1), while larger values concentrate λ around 0.5.\n\nWe conducted ablation studies on prostate dataset as shown in the table below, systematically evaluated α values across the range of 0.1 to 0.3. The results demonstrate minimal performance variance across these parameter settings, with α=0.1 emerging as the most stable configuration, consistent with the optimal parameters identified in prior MixStyle implementations.\n\n|α|0.1(**Ours**)|0.1|0.3|\n|:-:|:-:|:-:|:-:|\n|**Dice $↑$**|83.09|82.69|82.39|\n|**ASD $↓$**|3.06|3.31|3.49|", "claims_and_evidence": "1.\tThe UFDM module requires a more detailed explanation, as Figure 3a is somewhat ambiguous in conveying its functionality.\n2.\tThe operation mixing the statistical feature parameters of the vision foundation model with Gaussian noise, as opposed to directly substituting the specialized model's feature parameters, needs further elaboration.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria are well-suited for the problem and application at hand. The usage of benchmark datasets, such as fundus and prostate imaging datasets, is appropriate for evaluating the method's segmentation performance and generalization capabilities. Additionally, the evaluation metrics, including inference speed and parameter count, are relevant for assessing the practicality and efficiency of the proposed approach in real-world medical scenarios.", "theoretical_claims": "I have reviewed the theoretical claims related to the UFDM module and the spectral transformation properties. Specifically, I examined the statistical parameter transformation within the UFDM module and its justification in the context of domain generalization. The idea of integrating Gaussian noise with statistical feature parameters  is well-founded and aligns with existing literature on robust feature learning. Additionally, the decomposition and filtering of phase and amplitude components in the spectral domain are reasonable, as phase information is known to capture structural details while amplitude relates to global characteristics. These theoretical underpinnings are consistent with established principles in signal processing and domain adaptation, supporting the validity of the proposed approach.", "experimental_designs_or_analyses": "1 The hyperparameter a of the beta distribution in the UFDM module is set to 0.1 without experimental justification. \n\n2 On which layers is UFDM applied, and why these specific layers?\n\n3 On which layers of both the specialized and foundation models is LRA applied, and what is the rationale for selecting these layers?", "relation_to_broader_scientific_literature": "This paper contributes to the broader scientific literature by bridging the gap between large-scale vision foundation models and domain-specific medical imaging tasks. The proposed TinyMIG framework is inspired by recent advancements in foundation models, such as GPT and SAM, which have shown remarkable generalization abilities. However, unlike prior work that primarily focuses on direct fine-tuning or adaptation, this paper introduces a novel knowledge distillation strategy that emphasizes both global feature distribution consistency and local fine-grained alignment. \n\nThis approach is reminiscent of techniques like AdaIN for style transfer and frequency-domain methods for domain adaptation, but it uniquely combines these ideas to address the specific challenges of medical imaging.", "essential_references_not_discussed": "Frequency based domain generalization has been widely studied in image processing and machine learning. In the related work, please discuss the novelties with some strongly related works, such as:\n\n[a] \"Learning generalized segmentation for foggy-scenes by bi-directional wavelet guidance.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 2. 2024.\n\n[b] \"Learning spectral-decomposited tokens for domain generalized semantic segmentation.\" Proceedings of the 32nd ACM International Conference on Multimedia. 2024.\n\n[c] \"Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation.\" Annual Conference on Neural Information Processing Systems, 2024.", "other_strengths_and_weaknesses": "- Strengths\n\n1.\tThe paper introduces an innovative knowledge distillation framework designed to transfer generalization knowledge from large vision foundation models to specialized segmentation models. This approach is both novel and practically significant, as it enhances the efficiency of model deployment in medical applications.\n\n2.\tThe authors propose a dual-level distillation approach, which includes learning the global feature distribution priors from the large model and distilling domain-invariant features from each layer. This comprehensive strategy significantly improves the learning effectiveness of the smaller model.\n\n3.\tThe method achieves impressive segmentation results on two medical imaging tasks, along with a notable improvement in inference efficiency, making it highly suitable for real-world clinical deployment.\n\n4.\tThe paper provides extensive ablation experiments, including detailed quantitative analyses, model parameter comparisons, and inference efficiency evaluations. These thorough experiments offer valuable insights into the method's robustness and effectiveness.\n\n- Weaknesses\n\n1.\tThe UFDM module requires a more detailed explanation, as Figure 3a is somewhat ambiguous in conveying its functionality.\n\n2.\tThe rationale behind mixing the statistical feature parameters of the vision foundation model with Gaussian noise, as opposed to directly substituting the specialized model's feature parameters, needs further elaboration.\n\n3.\tThe hyperparameter a of the beta distribution in the UFDM module is set to 0.1 without experimental justification.", "other_comments_or_suggestions": "No", "questions_for_authors": "1.\tOn which layers is UFDM applied, and why these specific layers?\n\n2.\tOn which layers of both the specialized and foundation models is LRA applied, and what is the rationale for selecting these layers?\n\n3.\tThe hyperparameter a of the beta distribution in the UFDM module is set to 0.1 without experimental justification.", "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:

Meta-review: The paper received positive reviews unianimously from four experts. The rebuttal further boosts the final score. Accept.
Decision: Accept (poster)