Neighborhood Self-Dissimilarity Attention for Medical Image Segmentation
Authors: Chen Junren
Rui Chen
Wang-wei
Junlong Cheng
Gang Liang
zhanglei-scu
Liangyin Chen
Venue: NeurIPS 2025 spotlight ()
Keywords: Digital Health Equity
Medical Image Segmentation
Prior Knowledge for AI
Efficient and Scalable Vision
Attention Mechanism
Primary area: applications
TL;DR: We propose a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA) mechanism that enhances neural network segmentation accuracy by focusing on element-neighborhood differences without adding parameters.

Abstract: Medical image segmentation based on neural networks is pivotal in promoting digital health equity. The attention mechanism increasingly serves as a key component in modern neural networks, as it enables the network to focus on regions of interest, thus improving the segmentation accuracy in medical images.  However, current attention mechanisms confront an accuracy-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. Such a contradiction inherently restricts the real-world deployment of attention mechanisms in resource-limited settings, thus exacerbating healthcare disparities. To overcome this dilemma, we propose a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA), inspired by radiologists' diagnostic patterns of prioritizing regions exhibiting substantial differences during clinical image interpretation.  Unlike pairwise-similarity-based self-attention mechanisms, NSDA constructs a size-adaptive local dissimilarity measure that quantifies element-neighborhood differences. By assigning higher attention weights to regions with larger feature differences, NSDA directs the neural network to focus on high-discrepancy regions, thus improving segmentation accuracy without adding trainable parameters directly related to computational complexity.  The experimental results demonstrate the effectiveness and generalization of our method. This study presents a parameter-free attention paradigm, designed with clinical prior knowledge, to improve neural network performance for medical image analysis and contribute to digital health equity in low-resource settings. The code is available at [https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention](https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention).

Neighborhood Self-Dissimilarity Attention for Medical Image Segmentation
Junren Chen', Rui Chen', Wei Wang', Junlong Cheng', Gang Liang*, Lei Zhang'", Liangyin Chen
College of Computer Science, Sichuan University, Chengdu, China
2 Department of Electronic Engineering, Tsinghua University, Beijing, China
School of Automation, Chengdu University of Information Technology, Chengdu, China School of Cyber Science and Engineering, Sichuan University, Chengdu, China {lianggang, zhanglei, chenliangyin) edu. cn
Abstract
Medical image segmentation based on neural networks pivotal in promoting digital health equity. The attention mechanism increasingly serves as key compo- nent in modern neural networks, as it enables the network to focus on regions interest, thus improving the segmentation accuracy in medical images. However, current attention mechanisms confront an accuracy-come couracc-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. Such a contradiction inherently restricts the real-world deployment of attention mechanisms in -oourre-limited settings, thus exacerbating healthcare disparities. To overcome this dilemma, we propose a parameter-free Neighborhood Seff-Dissmmilaitt Attention (NSDA), inspired by radiologists' di- agnostic patterns of prioritizing regions exhibiting substantial differences during clinical image interpretation. Unlike shirwis-simiilartt--be anisms, NSDA constructs a size-adaptive local dissimilarity measure that quantifies element-neighborhood differences. By assigning higher attention weights re- gions with larger feature differences, NSDA directs the neural network to focus on gigh-disceepancy regions, thus improving segmentation accuracy without adding trainable parameters directly related to computational complexity. The experimen- tal results demonstrate the effectiveness and generalization of our method. This study presents parameter-free attention paradigm, desi designed with clinical prior knowledge, improve neural network performance for medical image analysis and contribute to digital health equity in low- low-resource settings. The code is available at https://github.com wtwpgiihuuuoooo/ohheeuueeee Lawirrn--LL/eeggbbrooooo------
self-attention mech-
1 Introduction
Medical image segmentation is pivotal in modern healthcare by enabling precise delineation of anatomical structures and radiological abnormalities within medical images [47]. This segmentation
provides clinicians with valuable imaging information for personalized decision-making thereby enhancing patient care and outcomes [T]. However, in resource-limited regions where expert-level radiological expertise is scarce, suboptimal medical image segmentation constitutes a significant barrier to equitable care [16], often leading delayed inaccurate diagnoses for complex conditions like multi-organ segmentation and tumor delineation [ 45). Such disparities create a diagnostic expertise gap between resource-rich and resource-por poor healthcare systems, impeding equitable access to precision diagnostics (35] To bridge this healthcare divide, neural networks have emerged as
*Corresponiigg authors: Gang Liang, Lei Zhang and Liangyin Chen
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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They are capable of automatically learning complex feature patterns from medical images [3I] thereby overcoming the limitations of traditional manual methods. which are time-consuming and rely on clinical expertise. Therefore, neural networks can potentially promote digital health equity and become the dominant approach in medical image segmentation
transformative solutions 12
21.
Attention mechanisms [23] inspired by the human visual system [1S 34]. are now essential for improving medical image segmentation in modern neural networks 667. These attention mechanisms recalibrate the channel-wise or spatial importance within the feature map, enabling the network to selectively focus on the Regions of Interest (ROIs), thus improving the segmentation accuracy in med- ical images [49]. Modern attention mechanisms widely adopt advanced feature extraction strategies such as mulli--baanchhh architectures [41], multi-scale processing (52]. large-kernel convolutions [42], and self-attention operators [25] to enhance accuracy in neural networks. However, such sophisticated designs inevitably increase model complexity, hindering practical deployment in low-resource set- tings (20), thereby compelling practitioners to forgo integrating these advanced attention mechanisms into their neural networks [6]]. Although lightweight architectures reduce computational demands through compact convolution operators (e.g.. depthwise separable convolutions) 7 group channel processing [52 62), and sparse sampling strategies [39], such architectural simplifications often result in coanse-grained feature representations. This representational degradation can impair segmentation accuracy in medical images, as the accurate delineation of subtle radiological abnormalities and anatomical structures relies fine-gra fine-grained features [ 4 Consequently, the design of attention mechanisms confronts a fundamental architectural conundrum: the endeavor to improve segmentation accuracy runs counter the maintenance of computational efficiency. This accuracy-complexte trade-off paradox exacerbates healthcare disparities by limiting equitable access to high-accuracy attention mechanisms for neural networks in resource-constrained environments.
To overcome the accuracy-complexity trade-off paradox, we propose a parameter-frre Neighborhood Self-Dissimilarity Attention (NSDA) mechanism for neural networks. This approach focuses on seement-neighborhor differences to boost segmentation accuracy without adding trainable param- eters directly related to computational complexity. The proposed NSDA is inspired by two key principles in radiologists' image-readee reading practices [60] (i) Neighborhood Inspection (59]. It rep-
resents a co-ottetuall observational paradigm, wherein radiologists begin by focusing specific element (c.g.. a pixel) or ROI in an image and then expand their attention to the neighborhood to synthesize contextual clues. (ii) Difference Prioritization 2]. Radiologists prioritize differences in medical images, as these features constitute essential biomarkers for clinical decision-making Accordingly, we devise dissimilarity measure based on complement of Gaussian kernel to quantify the feature differences between each element and its neighborhood in the feature map. By assigning higher attention weights to regions with salient feature differences, this approach enhances segmenta- tion accuracy. Notably, most existing difference-based approaches for medical image analysis operate in the multi-image paradigm 8 26 40), relying on multiple feature images to characterize inter- imeee differences. Unlike these methods requiring inter-image analysis, NSDA processes local neighborhoods within single images, thereby avoiding multi-image comparisons yet retaining sensitivity to fine- -e---graindd differences. Furthermore, given the clinical prior knowledge that diagnosti- cally critical patterns are often localized [19 68) we introduce Dynamic Neighborhood Scaling (DyNS) strategy for NSDA. DyNS adaptively regulates the neighborhood window size in NSDA across network hierarchies to prevent feature homogenization induced by aggregating long- onage contextual information. Unlike pairwise-similariy siiiiaiitiiiaaaaaaeeeee -bsed self-attention mechanisms 225 39 57]. the
proposed NSDA focuses on element-neie neighborrggoo dissimilarity without adding parameters. Our padiologist-visionnnnspr attention mechanism not only preserves original network complexity, but also demonstrates the potential to advance digital health equity in low-resource settings.
In summary, the main contributions of our study are as follows:
Inspired by radiologists' neighborhood inspection and difference prioritization, we propose parameter-free Neighborhood Sellf-issimilarity Attention (NSDA) that enables neural networks to focus on element-neighhorrhood differences, thereby boosting segmentation accuracy.
We introduce a Dynamic Neighborhood Scaling (DyNS) strategy in NSDA, which adaptively adjusts NSDA's neighborhood window size across network hierarchies to prevent feature homogenization caused by aggregating excessive long-range information.
Experimental results demonstrate that NSDA outperforms traditional attention mechanisms, high- lighting their effectiveness and potential to promote digital health equity in low-resource settings.
2

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Neighborfhojo Inspectionn
Neigtoorhaod Average 17y)
Goussian Karne Based Disdmilarty
Signold Funstion
Attantion Fusion -f
Elmmett (Xc)
antnin o/
0y
Difference Prioritizaion
Ini?)
60
(8)
Figure I: The Overview of our Neighborhood Self-Dissimilarity Attention (NSDA). (a) The proposed architecture of the NSDA illustrates how a Gaussian-kernll-baan measure quantifies element- neighborhood dissimilarity, thereby assigning higher weights elements with more salient dif- ferences. (b) The dissimilarity- weighting curve generated by NSDA. (c) The conventional sigmoid-based weighting curve employed in traditional attention mechanisms. Compared with traditional sigmoid-basct methods, NSDA eliminates sign-induced bias, thereby ensuring equitably weighted contributions from features with opposing polarity.
2 Related Work
has trig- gered revolutionary trend in computer-aided diagnosis, demonstrating great potential to democratize access to precision medicine in sesource-constraiing clinical environments 12] However, due to the inherent inductive bias of Convolutional Neural Networks (CNNs), U-Nets struggle to model Long-range contextual relationships [64], limiting their ability to comprehensively characterize ROIs. To mitigate this limitation, subsequent U-Net variants expand receptive fields via parallel or cascaded modules 12 52). More recently, advanced approaches [10] 24 32 46) integrate Vision Transformers
Medical Image Segmentation based on data-driven U-shaped Networks (U-Nets) 4 53
(ViTs) Mamba architectures to model global information for enhanced contextual awareness.
However, most existing methods still process all regions of an image equally during feature learning, failing pay attention to diagnostically vital ROIs, and thus achieving limited segmentation accuracy, To bridge this gap, attention mechanisms have emerged as pivotal solution
233 allowing improved segmentation accuracy by guiding networks focus on salient regions in complex scenes 14
Visual Attention Mechanisms in neural networks are broadly categorized into channel and spatial at- recalibrating feature map weights to prioritize ROIs [49]. Channel attention mechanisms aggregate channel information via global pooling to generate compressed descriptor vectors [3] (50
tention 23
These vectors undergo nonlinear transformations through convolutions or Multi-Layer Perceptrons (MIPs) before yielding channel weights by a sigmoid function, enabling networks to prioritize semantically relevant targets [30]. However, these mechanisms inherently lack spatial perception capabilities [63]. Spatial attention mechanisms address this limitation by capturing positional depen- dencies between pixels tokens p [2)] For instance, established self-attention architectures 255 39)
partition feature maps into tokens and compute pairwise similarity matrices to model global contex- tual relationships. While subsequent mechanisms primarily enhance feature representation based on channel or spatial dimensions 7 52 62). these methods rely on computationally intensive operations such as convolutions and MLPs, thus rendering them impractical for resource mnbourre-limited limited settings and
expanding the healthcare access divide [61]. Recent lightweight alternatives [55, 66) use simplified operations such as compact operators, partial computation, or sparse sampling to reduce complexity, yet often degrade fine-grained feature representation critical for medical image segmentation [12]. leading to suboptimal accuracy gain for neural networks. Thus, overcoming the accuracy-complexity trade-off paradox is an urgent challenge in the research of attention mechanisms.
3 Methodology
Overview. We propose andiologist-vision visigii--iiiionn-iieeeeeedd NSDA, which guides neural networks to focus on neiment-nigghooroood differences to enhance segmentation accuracy while overcoming the accuracy- complexity trade-off paradox. Figure [ illlustrates schematic of the proposed NSDA operating on individual elements of feature map. First, we design a Dynamic Neighborhood Scaling to adaptively regulate NSDA's neighborhood boundaries across network layers, preventing feature homogenization caused by aggregating excessive long-range information. Second, we simulate radiologist's neighborhood inspection by extending the analysis scope from each element to its
3

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neighborhood. thereby aggregating contextual information. Third, we construct a dissimilarity measure that quantifies the dissimilarity between each element and its aggregated neighborhood representation, which in turn modulates attention weights based on the degree of dissimilarity, thus modeling the radiologist's difference prioritization. Finally, we perform Attention Fusion on the original element and attention weight to obtain the final output. Each element undergoes the aforementioned operations to generate attention-auge -augmeen- feature maps, enabling clement-wise discriminability critical for fine-grained medical image segmentation.
Dynamic Neighborhood Scaling. Traditional attention mechanisms capture the global context through global pooling or self-attention operations. However, in medical imaging, ROIs such as lesions or anatomical structures often occupy small spatial footprints, but this constraint is ignored by most existing approaches, leading to a suboptimal segmentation accuracy improvement for neural networks. Although straightforwardly constraining attention to a fixed-size window may initially seem beneficial, such static window gradually exceeds the feature map size as the network depth increases, homogenizing critical local feature representations. To address this issue, we design a simple Dynamic Neighborhood Scaling (DyNS) strategy that adjusts the neighborhood window size in proportion to the resolution of the feature map (i.e., its spatial dimensions) at each network hierarchy. This process is formulated as follows:
Bt cc, JUL,Br, 8} E N4,
1, if [BL/S] is even
by
c= c= 12, 12, otherwise
where by and B2 denote the neighborhood window size (width/height) and the corresponding feature map size in the L-th network layer, respectively. The scale factor S is empirically set to 8. The constant e ensures bilateral symmetry by ensuring that by is an odd integer, which establishes equidistant left-right boundaries around target elements to facilitate centered neighborhood aggregation. As result, DyNS preserves fine-grained features while mitigating the feature homogenization arising from the aggregation of extraneous global contexts, which is crucial for segmenting small ROIs.
Neighborhood Inspection. In medical image analysis, the semantic interpretation of individual elements (e.g., pixels) is primarily determined by localized contextual patterns rather than long-range dependencies. Although natural images benefit from global region interactions for holistic scene understanding, medical imaging exhibits a distinct behavior in which excessive reliance on long-range dependencies introduces feature homogenization. Inspired by radiologists' neighborhood inspection (i.e., the woaalto-cootextull observational paradigm), we model per-element neighborhood contextual dependencies in feature maps. This design simulates clinical reasoning by prioritizing fine- grained patterns within constrained anatomical regions, effectively avoiding feature homogenization while retaining discriminative details essential for segmentation. Specifically, for a given target element Ji.j. we aggregate features from its centered by. x by window. As shown in Figure [ this process generates two statistical metrics: an average 1/i, encoding the contextual representation of the neighborhood window (69] and a variance of, quantifying the feature variability within the localized neighborhood (33. This process is expressed as follows:
itl Σ
Σ
(Im,n),
1
Σ
it
Σ
iti
(Im,n v(.) 21=
by. -
Yis by by n=i-l
Py
X bL p==i-mms
2
m=jj-
where i,j E N+ denote the 2D spatial coordinates in the feature map and E N4 defines the minimum distance from the element Zi,j to the boundaries of its local neighborhood window.
Difference Prioritization. The dissimilarity between element and its neighborhood depicts the presence of complex high-frequency patterns, such as textural variations, within localized regions. This observation implies that quantifying discrepancies, as opposed similarities, offers heightened sensitivity in detecting subtle anatomical boundaries and radiological anomalies. Motivated by radiol- ogists' image-reading practice of prioritizing discrepancies, we propose a novel dissimilarity measure that adaptively assigns higher attention weights to elements exhibiting more salient deviations from their neighborhood context. Although the Euclidean distance widely used as a dissimilarity mea- sure, it violates two fundamental requirements for attention mechanisms [23 49 671: (i) a nonlinear response to feature variations and (ii) a bounded output within [0,1]. 1]. In contrast, the Gaussian kernel 66, 36) satisfies both requirements, yet intrinsically captures similarity, not dissimilarity. To address this issue, we derive our dissimilarity measure by the complement of the Gaussian kernel. Specifically, we first compute the Gaussian kernel-based similarity score between the element x.,

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and its contextually aggregated neighborhood representation Vi,- Here, Vi, is obtained by the local average pooling [3] 69]. We then derive dissimilarity score, exploiting the complementarity constraint between similarity and dissimilarity scores, which sum to 1. The above process generates the corresponding attention weight di.j E [0, 1] for element Fi,y as follows:
aijj = DiSim((r.)) M... (.,) - Sim ( Wi.s G. 11 (-11
(- 20
where Vi,j and of are derived directly from Equation 2 The function Sim(() [0, represents the Gaussian kernel, which quantifies the similarity between Fi, and 3i. [6]. The proposed Gaussian- kernel-based dissimilarity measure ensures adherence to the boundary conditions of the attention score while strictly enforcing the complementarity constraint of similarity.
Attention Fusion. The attention mechanism is typically integrated into neural networks by element- wise multiplication between weight matrices A E R XW and feature maps X E R "xx However, in this conventional fusion method, the weight as Tij. which diminishes the feature representation. To address this limitation, inspired by 27 27 49
falls the range [0, 1], resulting 24, a...a.a. <
we incorporate a residual connection for the attention fusion. For target element T4,5 with its associated attention weight di.j. the attention-augment feature is yielded via:
Z Fi. ai + Zi.j-
The proposed method performs element- wise attention fusion per feature map. Given an input feature map X E " and its corresponding attention weight matrix A E R f according to Equation the final output feature map Z E IR Hx is computed through:
21,1
T1WW
a1,1
a1,W
Z = OX x=
A
(5)
FH,1
Z.W
aH,,1
where and denote element multiplication and element-wise addition, respectively. Our method climinates learnable parameters (c.g., convolutional layers or MLPs) directly related to computational complexity, thus preserving its compatibility in resource-limited deployment scenarios.
4 Experiments
Objective. We perform experiments to study the ability NSDA, designed to address the following research questions (RQs):
RQ1: How does our Neighborhood Self-Dissimilarity Attention (NSDA) behave in the neural
networks for medical image segmentation, compared with the traditional attention mechanisms?
RQ2: Does the inference mechanism of NSDA possess explainability?
RQ3: How the generalizability of NSDA across heterogeneous architectures and tasks?
RQ4: How does the neighborhood window size in NSDA affect segmentation accuracy, and Dynamic Neighborhood Scaling (DyNS) effective?
RQS: How sensitive the scale factor in DyNS to segmentation accuracy?
RQ6: How does the variance coefficient in NSDA's Gaussian kernel affect segmentation accuracy?
RQ7: Can dissimilarity in NSDA be quantified using other measures, like the Euclidean distance?
RQ8: Which is more critical for medical image segmentation in NSDA, similarity or dissimilarity?
Datasets. We evaluate the effectiveness of NSDA on three prominent medical image segmenta- tion benchmarks with diverse modalities and scales: Synapse (multi-organ abdominal CT) (45). ACDC (cardiac MRI) [ and BUSI (breast ultrasound) [1] To thoroughly assess generalization capabilities for the proposed NSDA, extend validation to COVID-19 pneumonia lesion detec- tion (CPLDet) [5I], endoscopic bladder tissue classification (EBTCIs) [38), and natural image segmentation (VOCSeg, combining PASCAL VOCO7 [17] and VOC12 665).
Baselines. conduct a comprehensive comparison, we not only compare the well-known attention mechanisms in medical image tasks like SE [30], CBAM [63], and MSCAM (52,, but also compare
5

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Table 1: A quantitative comparison of the proposed NSDA and other baselines integrated into segmentation networks (U-Net NNet, UNeXt, and TinyU-Net) across datasets (Synapse, ACDC, and BUSD, using mean DSC (%) for main evaluation metrie. The highest score is marked in bold.
Network
Params
FLOPs
1pur
Daraset
Netwokk
Params
FLOPs
1put
Dataset
(M)
(G)
(FPS)
Synapse
ACDC
BUSI
(M)
(G)
(FPS)
Synapse
ACDC
BUSI
U-Net [S3
19,5820
101,7706
298.34
78.18
91.77
71.60
TransNee [0]
93.2317
61.6085
68.26
79.89
92.49
72.70
SE 30
19.7018
90.53
SE 30
93.4145
64.6822
CBAM
117.49
78.05
90.85
CBAM
6466667
79.23
92.41
BCA 61
19.5820
8023
251.74
76.48
90.60
69.89
ECA [
93.2317
61.679
61.70
79:15
92.48
68.15
BAM 50
19.8054
102 6914
150.34
77.48
92.03
71.23
BAM 500
93.7061
65.0218
52.75
79.04
92.46
72.94
SinAM
b6
19.5820
101.7706
242.82
77.29
91.68
70.97
SimAM 66
93.2317
64.6085
64.40
79.76
92.35
67.63
CA A9
19.6821
8018
166.37
76.00
90.0-
70.34
CA 29
93.3766
61.6368
59.14
79.62
91.61
31.52
GC 2
20.0579
8043
210.49
77.50
91.38
9
93.9692
64.6199
82.90
92.42
73.89
119,4573
91.56
$1.55
71.12
MWSA
19,8966
105.8 8307
91.26
43.23
72.73
55.25
MWSA MLKA
98.7160
61.6284
58.46
71.76
92.13
68.56
MLKA X3
25.2917
121 6817
132,89
79.22
92.35
71.34
98.6999
73.0168
$4.59
79.
92.46
73.38
CGA L3
20,0600
104.9455
159,53
75.01
90.45
70.53
CGA
93,8239
65 65.5551
56.72
S0.68
92.46
71.96
CAA
21.5718
111.6123
183.90
77.72
90.79
7L.03
CAA
96.1244
68.9593
60.50
90.46
92.42
71.27
CMO 2X
22,4800
115.5456
92.15
CMO
97.6486
70.7833
79.57
72.28
MSCAM 52
23:7311
122.9100
95.54
91.40
MSCAM 52
73,7508
19:03
86.03
71.07
NSDA (Ours)
19,5870
101.7706
130.81
81.62
92.56
74.46
NSDA (Ouns
93.2317
64 6085
63.51
X3.81
V2.77
75.29
UNeXi 58
1,4721
1,1628
238. 36
72.40
89.18
63.71
T-Net-NNet
0-4816
3.3855
172.42
78.33
91
72.24
SE 30
1.4743
1.168
203.38
71.87
88.74
60.38
SE 3D
0.5687
3.4171
141.93
72.48
91.56
71.24
CBAM GV
1.4747
1,1878
130.76
71.64
87,97
40.34
CBAM M
0.5715
3.4514
100.90
79.23
90.14
70.82
1,1668
79.53
BAM 50
73.17
89.11
BAM
0.7097
4.2795
79.00
91.66
71.65
SimAM 66
1.4721
1.1628
208.87
70.78
85.15
61.79
SimAM DO
0:1816
3.3855
145.11
78.03
91.47
70.29
CA 20
1.4767
1.1719
157.17
96
69.59
20.15
CA P
0.5564
3.4595
113.92
80.05
91.67
71.91
GC g
5.7934
1.1763
180.54
72.32
89.17
65.18
GC U
0.8251
3.4187
130.87
78.31
92.00
71.57
MISA P5
1.5433
1,5655
168.57
72.29
89.01
62.45
MIIS
MS
3.27116
20.5653
122.07
$0.02
92.07
71.23
MWSA
1,4950
60.75
19,80
MWSA
0.7301
7.4120
73.24
90.53
66.35
1,5596
2,0617
143.39
72.35
88.23
MLKA
09
3.1991
22.7904
90.29
92.11
71.89
CGA 03
1,8944
1.5732
153.03
73.03
89.61
60.28
CGA
L
0.8487
6,5342
109.02
79.21
92.22
72.20
CAA
1.5127
1,4860
184.00
51.63
79.25
$1.25
+ CAA
1.9319
12.9506
125. 17
80.37
92.37
71.34
CMO 23
1.5273
1.5002
173.81
50.81
86.62
28.30
CMO 28
2.3955
16.5535
122.98
79.58
92.34
71.46
MSCAM 52
1.5593
2,0073
11.29
10.01
20.68
MSCAM 54
3,5083
23 9667
75,90
37.84
79.83
71.89
NSDA (Cuurs)
1,4721
90.18
NSDA (Ours)
0.4816
3,3855
75,09
the outstanding attention mechanisms in natural image/remote sensing image tasks such as CA MLKA 662]
29
CAA [7]. and sertwise-immilartty-eeee self-attention mechanisms like MHSA 25).
MWSA
391 To compare attention mechanisms that can be deployed in -ssurrce-limited settings,
incorporate lightweight variants (e.g., ECA 61.. SimAM (66) for baseline comparison.
Implementation Details. Attention mechanisms are typically integrated into the final layer of feature extraction modules in established medical image segmentation architectures (U-Net (53) TransUNet 100, UNeXt [58], and TinyU-Net (12) following conventional integration paradigms 49] This conventional integration scheme preserves the architectural integrity the backbone networks. We conduct experiments
NVIDIA GeForce RTX 4090 GPU using the PyTorch framework. The models are trained for 300 epochs using the Adam optimizer (37), with a composite loss function that combines cross-entropy and Dice loss 12]. The initial learning rate is set to 1 x 10 and decayed using a cosine annealing scheduler, with a minimum value of 1 x 10 Synapse and ACDC replicate TNans setup [4 45). while EBTCIs adopts the method from
38 for processing images. We adopt different splits for each benchmark: a 6:2:2 ratio (train/validatiinntess) for BUSI, an 8:1:1 ratio for NCPDet, and 9:1 train-test split for VOCSeg. All input images are resized to 256 x 256 resolution. Following the training strategy proposed in (22), data augmentation (flip, rotation) is performed for the first 270 epochs and turned off for the last 10% of training.
To evaluate model performance, we use the following evaluation metries: the Dice Similarity Coefficient (DSC) for segmentation tasks [12), the mean Average Precision (mAP) for detection tasks [22), and the Top-1 Accuracy (Top-1 Acc), the mean Recall (mRec), the mean Precision (mPrec) for classification tasks. The computational efficiency of the model is assessed using the number of Parameters (Params), Ploating-Point Operations (FLOPs), and Throughput (Tput) to quantify model size, computational complexity, and inference speed, respectively. The reported FLOPs correspond to twice the Multipyy-Acumuilat Operations (MACs) measured by THOP [22].
Evaluation Metries.
5 Results and Discussion
Quantitative Analysis (RQ1). Table T presents a comprehensive comparison of established neural network architectures integrated with distinet attention mechanisms in three benehmark datasets for medical image segmentation. From these results, we have the following observations:

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NSDA achieves the most significant performance gains for neural network architectures across medical image segmentation benchmarks. The experimental results demonstrate that our method achieves consistent ofae-offthhe-ar performance improvements integrated into various segmentation networks (U-Net, TNet UNeXt, and TinyU-Net) across multiple benchmarks (Synapse, ACDC, and BUSI). Specifically, when integrated into these network architectures, NSDA consis- tently achieves higher mean DSC (mDSC). For instance, the ASDA-auumented UNeXt achieves substantial gains of +4.99%. 1.00%, and +3.89% in mean DSC over the original UNeXt on the Synapse, ACDC, and BUSI datasets, respectively, significantly outperforming other attention base- lines. The observed performance gains stem from NSDA's element- wise neighborhood modeling. It prevents feature homogenization through adaptive context aggregation and fine- fine-graine grained feature representations. Our NSDA guides the network's s attention toward regions with salient differences, thus enhancing its ability to detect complex anatomical structures and subtle radiological anomalies.
Traditional attention mechanisms demonstrate limited efficacy in optimizing neural networks for medical image segmentation. As evidenced by Table IT 168 baseline resutls of attention- integrated architectures reveal that 79% (132 out of 168) underperformed their original networks in medical image segmentation tasks. Notably, UNet achieves superior segmentation accuracy despite containing 91M more parameters than the lightweight UNeXt [T). However, most attention modules with parameter counts below this threshold degrade segmentation performance, exposing fundamental flaws in conventional attention architectures rather than overfitting. These methods depend on global computation operators (e.g., pooling, self-attention), which induce excessive feature homogenization during contextual aggregation. progressively eroding anatomi- cally critical patterns crucial for precise delincation. Unlike traditional attention mechanisms, our approach addresses the limitation of coarse-grainge feature representation by explicitly modeling element- neightooooihh dissimilarity to capture fine-grained differences. This strategy effectively guides the network's 's focus toward regions of interest (ROIs), enhancing segmentation accuracy.
NSDA has the potential to promote digital health equity in resource-limited settings. Compared with traditional attention mechanisms and lightweight variants, our NSDA overcomes the long- standing eecuracy-complette
trade-off paradox, achieving optimal performance in segmentation ac- curacy, parameter count, computational complexity (Table For example, the -agmmenneett U-nt preserves the architectural compactness of the original model (0.48M parameters, 3.39G FL.Ops) yet elevates segmentation accuracy by +3.24% mean DSC with slight inference speed reduction on the Synapse dataset. Remarkably, NSDDA-qqipped U-nyU-Net exhibits 99. 99.49% fewer parameters and 94.75% lower computational complexity than TransUNet (0.48M vs. 93.23M Params; 3.39G vs. 64.61G FLOPs), while delivering 1.85 x faster inference speed ( (126.29 vs. 68.26 FPS) and superior mean DSC scores on Synapse (+1.68%), ACDC (+0.24%), and BUSI (+2.39%). This efficiency stems from NSDA's parameter-free design, which eschews resource-intensive opera- tions such as convolutions or multi-layer perceptrons. Instead, it employs neighborhood aggregation based on PyTorch framework alongside a -auussian-kemel--ee ome-grained differences vital for medical image analysis. Such parameter-fres free architecture enables seamless integration into diverse backbones of neural networks and empowers lightweight models (e.g., UNe TinyU-Net to approximately rival the accuracy of resource-intensive models (e.g.. TratsUNet) - a critical advancement toward digital health equity in resource-limited settings.
dissimilarity measure to focus on
Qualitative Analysis (RQ2). To examine the explainability of attention mechanisms, we utilize Grad-CAM [56] visualize the inference mechanism of these attention- inttegraee neural networks. Figure presents comparative qualitative results of various attention-integrated U-Net variants on three medical imaging benchmarks. Grad-CAM visualizations reveal that our NSDA improves U- Net's ability to segment complex ROIs (e.g., the pancreas, right ventricle, and benign breast tumors), which the baseline U-Net fails to segment adequately. Qualitative segmentation results demonstrate that 'SDA-augmenter U-Net achieves significantly improved agreement with ground truth labels. These results highlight NSDA'S outstanding explainability in medical image segmentation. This high explainability of NSDA can be attributed to the explicit embedding of radiologists' prior knowledge (Neighborhood Inspection and Difference Prioritization) into NSDA's architectural design.
Generalization Evaluation (RQ3). We substitute the original attention module in the attention- augmented architecture (AtU-Net (48) with NSDA to further assess its architectural generalization as drop-in replacement. We find that NSDA reduces AttU-Net's 's computational complexity while enhancing segmentation accuracy compared with its original attention module (Table Moreover, NSDA achieves consistent accuracy gains across various network architectures, demonstrating its
7

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Image (top)
U-Net
NSDA
SE
ECA
MHSA
SimAM
CBAM
MSCAM
Synapse
Right Kamw
Galbbeder
Liver
Anrta
ACDC
Myocadim
Wervidee
BUSI
Derign
Figure 2: Qualitative results across various benchmarks. Odd rows: Grad-CAM [56 visualizations of attention-inten -inttrrnn-ttteetted U-Nets for segmenting the pancreas (Synapse [45)), right ventricle (ACDC (5),, and benign tumors (BUSI (I). Warmer colors (e.g., red) indicate higher attention weights. Even rows: segmentation outcomes of attention-integrated U-Nets.
Table 2:
Generalization performance of attention mechanisms across medical image tasks and natural image segmentation task. ArigAttn denotes the original attention block in AtU-Net. Attention roles: '+' (complement),
(substitute). The highest score is marked in bold.
Synapoe (Metical Image Segmettion)
Cl (Medical Desectio)
EII (Medical Clasticatio)
VOEScg (Natzaal Segnention)
Phrams
11.11%
Network
BAFTS
Narwort
Top-1 Acc
milr::
sthre
Netxort
mFA
OrgAit SE 33
34.56
138.51
77.61
SE [1]
$1.47
$2.63
09:30
SE Hg
35.64
$3.45
40:22
SE
41.09
$2.15
OraAAts-B BCA [i]
$4.51
150.51
77.38
-BCX e1
79.31
$3.65
47.42
ECA BT
35.61
48.38
42:34
BCC [:]
48.92
$1.58
OrgAits
[33
53.03
Smahdd
59.45
Osgalts CBAM [3
34.56
130.53
78.24
-BBAM [3]
90.56
$3.17
50.45
CBAM 03
54.46
$1.07
41.59
CSAM (3)
34.53
66
Oradita-
MSCAX [53
36.0
141.08
73.56
-MICAM 53
53.84
10:62
20.44
MSCAM 52
52:37
43.41
45:92
MSCAM [X]
30.47
11.33
Orgaliis
(0am)
NSDA (Dam))
55.83
(OMB))
NSDA (Oum)
cross-architecture generalization (Table []. To further evaluate NSDA's cross-task generalization capacity, we integrate the attention mechanism into three network architectures: U-Net [53] for natural image segmentation, YOLOX [22) for medical image detection, and ResNet34 (27) for medical
image classification. As shown in Table 2 NSDA consistently outperforms established attention
baselines (e.g.. CBAM [63], MHSA (25 across tasks. These results validate the generalization
of NSDA across architectures and tasks. The superior generalization of NSDA can be attributed to its dynamic neighborhood feature representation, which adaptively identifies input-dependent discrepancies between elements and their local context, rather than relying on static weights.
Ablation Analysis (RQ4). To investigate the role of DyNS in NSDA, we replace DyNS with static neighborhood window sizes (K e N4) in all network hierarchies. Since its complete removal leaves NSDA's neighborhood window size by (Equation I undefined, K E N4 must be manually set as static neighborhood window size. As shown in Figure 3 the segmentation accuracy difference (AmDSC) between NSDA-augmented networks with and without DyNS exhibits a bell-shaped trend on the multi-scale medical segmentation benchmark (Synapse). The AmDSC initially increases with the static neighborhood window size K, peaks at K = 31), and subsequently declines with further increases K. In addition, we find that aggregating features from a global region (K (H, W)) degrades the segmentation accuracy. These results reveal two fundamental limitations contextual modeling for medical image segmentation: (i) overly small-scale localized context aggregation fails to capture holistic structural patterns in ROIs, and (ii) excessively large-scale context

---

NSDA-eupmentedd U-Ne!
NSDA-auggmenleed TransUNet
NSDA-sagmentedd UNeXt
NSDA-augmerted TinyU-Nel
b1080 (h)
DIDSO (5)
Bydd
AnDeC (5)
w0..0iamm 2OLL EURA glightoo boffhood K
Neghboffiedd K
Naighorroood K
Figure 3:
The results (AmDSC (%)) of ablation analysis for DyNS on Synapse dataset. H E N and W E N, in K denote the height and width of the feature map, respectively. Each data point represents the segmentation accuracy difference (AmDSC) between DyNS-free NSDA-augmented network architecture and its DyNS-equipped counterpart.
Table 3: The results (mDSC (%) of sensitivity analysis for the scale factor S (Equaaiinn) I and the variance coefficient of Gaussian kernel (Equation 3 in NSDA on Synapse dataset. We report the mean and standard deviation (mean, ata) from three independent runs with different random seeds. and V denote U-Net and U-et with NSDA, respectively. The highest score is marked in bold.
Scale Factor in DyNS
Variance Coefficient of NSDA's Gaussian Kernel
Network
S 2
8
S 8 (Ours)
S= 16
S
2n (Ours)
4r2,
60/,
80. s0
79.96, 55
81.35p..a $1.090.4
78.94 ss
80.496.56 80.233.37
81.35g. 52 81.09a.4
78.98
78. 740.5
80. 80.54l..0
79.37o.32
79.51o.74
80.29
79.88o.4
80.0 80.01o.41
aggregation causes feature homogenization, which diminishes anatomically discriminative patterns. Therefore, both excessively large window sizes and overly small window sizes lead to suboptimal accuracy due their static nature, demonstrating the necessity of balanced context aggregation. Notably, as shown in Figure 3 all NSDA-augmented networks using the static neighborhood window size instead of the proposed DyNS exhibit consistent accuracy degradation (negative AmDSC) compared with DyNS-equipres ness of DyNS in our NSDA. These results stem from DyNS dynamically adjusting the neighborhood window size based on the resolution of the feature map at each network hierarchy, which mitigates feature homogenization caused by aggregating overextended contextual ranges.
NSDA -SDA-augmented networks, empirically confirming the effective-
Sensitivity Analysis (RQ5& & RQ6). We evaluate the individual effects of the DyNS scale factor S (Equation [ and the variance coefficient (Equation 3 for NSDA segmentation accuracy by systematically varying their values (Ta We observe that NSDA-augmented networks achieve optimal segmentation accuracy with a scale factor of S 8. The results in Table 3 further confirm the importance of the scale factor trade-off, which directly controls the size of the neighborhood window (Equation D. We attribute this a trade-off in neighborhood window size: an undersized neighborhood (large S) fails to capture essential context, while an oversized neighborhood (small S) leads to the dilution of discriminative local features. In addition, we find that networks achieve optimal segmentation accuracy with a Gaussian kernel variance coefficient of 2 (Table B) empirically supporting the effectiveness of this moderately low variance coefficient. Our NSDA's s variance coefficient of the Gaussian kernel aligns with the default setting in classical machine learning methods, such as SVM and PCA 6 36). The underlying mechanism is illustrated by the Gaussian kernel's weight profile in Figure [b). A larger variance coefficient produces broader, flatter curve that dampens sensitivity to fine- greigga differences, whereas a smaller coefficient yields a sharper, more localized curve that amplifies tiny feature differences.
Comparative Analysis for Similarity vs. Dissimilarity (RQ7 & RQ8). To investigate the relative efficacy of similarity versus dissimilarity in NSDA for medical image segmentation, we explore two alternative measures by rewriting Equation 3 a similarity measure (a:.j Sim) and a dissimilarity -errsiinn-eeel---aree
measure (as.j = EDiSim(-)) different from the proposed sure (a.,) DiSim(-)). Specifically, the Gaussian kernel directly used for the similarity measure, as it is the complementarity constraint for the proposed dissimilarity measure (Equation [3. While the Euclidean distance serves as a widely adopted dissimilarity metric, it inherently fails to satisfy two critical requirements of nonlinear transformation and [0, 1] range constraints in attention mechanism design conventions. To address this limitation, we introduce novel dissimilarity measure E.)Sim(() by applying sigmoid function to the Euclidean distance, which is standard method for producing
dissimilarity mea-

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Table 4: The results (DSC (%)) of comparative analysis for similarity vs. dissimilarity in NSDA on the Synapse dataset. O and V denote U-Net, TransUNet, UNeXt, and TinyU-Net with NSDA, respectively. The highest score is marked in bold.
Network Rewriting Equation
B
Mean
Spleen
Right Kidney
Left Kidney
Gallbladder
Liver
Stomach
Aorta
Pancreas
R.3 Sin(()
79.01
85,58
82.08
86.44
63.48
93.71
72.38
88.94
59.49
EDiSimm())
79.51
87.02
84.25
87.05
63.06
93.49
73.15
88.24
59.82
Our original equation
81.62
90.33
85.94
87.46
71.90
94.33
72.84
88.71
61.48
01. Sim (
81.14
86.20 26
83.77
89.36
70.94
95,20
72.39
89.57
61 64
EDiSim(
82.41
87.30
86.07
90.65
74.95
94,95
73.06
89.41
62.87
Our original equation
83.81
93.04
87.83
91.52
73.36
95.52
76.31
89.23
63.66
Sim(-)
75.68
86. 18
83.63
86.02
58.02
93.34
68.19
81.49
48.57
EDiSim(-)
75.72
88.62
82.53
86,40
52.22
92.88
67.14
83,08
52.91
Our original equation
77.39
88.27
84,70
88.58
64,52
93.45
64.89
82,98
51.73
Riy Sin
93.96
EDiSumi
80.74
92.81
86.33
89.47
66.64
93.54
71:33
87.99
57.79
original equation
81.57
92.
86.21
91.29
71.16
93.82
72.13
88.81
56.95
attention weights P3 In summary, the rewritten attention weight 0i, is expressed follows:
11 vi.all?) 20?,
Sim(xaa Va.j. o..,)
EDiSim(ar.j Vv.)) = Sigmoid([ - yk.
(6)
where Sim(-) E [0, 1] denotes Gaussian kernel for the similarity measure, and EDiSim (-) E [0, 1] denotes the proposed dissimilarity measure based on the sigmoid-activated Euclidean distance. As evidenced in Table 4 the dissimilarity-bar babseerrtt-- operators (EDiSim(-) and DiSim(-) (-)) effectively en- hance the segmentation accuracy compared to the similarity- based methods (Sim(-)) in the mean DSC (Table I Crucially, our Gaussian mouusian-keenel-aee kernel-based dissimilarity measure (Equation [3 achieves the most significant accuracy gains in neural networks for medical image segmentation. Although both EDiSim (-) and DiSim(.) are based on dissimilarity, they represent fundamentally different formula- tions: EDiSim(.) relies on a simple geometric distance with fixed nonlinearity, while DiSim(.) is derived from the complement of Gaussian kernel, which inherently encodes a probabilistic notion of dissimilarity and aligns better with the statistical properties of feature distributions medical images. saiisse-iiiilarit-bbbeeee self-attention
Furthermore, we find that the proposed NSDA outperforms mechanisms (e.g.. MHSA [25] and window-based based MWSA (39) in medical image segmentation (Table D. These findings suggest new insight that medical image segmentation tasks benefit more from dissimilarity than similarity, which aligns with clinical prior knowledge.
Limitation. Our method overcomes the accuracy-come complexity trade-off paradox in attention mecha- nisms, enabling neural networks to achieve better performance gains. However, deploying attention- augmented neural networks in low-resource settings hinges not only on the computational complexity of attention modules, but also on the size of the network architecture. Consequently, lightweight neural networks are critical to advancing digital health equity, particularly in sesource-limiter settings. Future research will focus on their efficiency and scalability to bridge healthcare access divides.
Conclusion
Inspired by radiologists' neighborhood inspection and difference prioritization during image interpre- tation, we propose a parameter-fis free Neighborhood self-Dissimilarity Attention in neural networks for medical image segmentation. Unlike traditional attention mechanisms, our method quantifies the dissimilarity in feature maps to generate attention maps that prioritize salient regions with high disparity, thus enhancing segmentation accuracy without adding parameters directly related to computational complexity. This endeavor inspires clinical prior knowledge for the attention mechanism, offering a practical method to promote digital health equity in resource- resource-limited settings.
Acknowledgments and Disclosure of Funding
This work is partially supported by Sichuan Science and Technology Program (Nos. 2026NSF. SCZY0032, 20252NSFSC0735, and 3025ZNSFSSC000))
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Reviews:
[{"review_id": "xpffG1H7Nu", "reviewer": "Reviewer_XYh8", "summary": "The authors propose a different, parameter-free, attention mechanism, NSDA, that is more efficient than pairwise-similarity based mechanisms and shows promisiong results for segmentation tasks. As part of NSDA, DyNS is proposed to constrain NSDA to localized information throughout feature hierarchies present in deeper networks. The authors emphasize that both, NSDA and DyNS, are inspired by patterns present in how actual radiologists' workflows. The paper has strong experiments to demonstrate the efficacy of the method and support parameter choices. \nHowever, the major shortcoming of the paper is that it is poorly motivated in that a more efficient attention mechanism for medical image segmentation tasks doesn't actually address any of the health equity concerns the authors claim to care about.", "questions": "N/A", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The experiments are thorough and inspire confidence in the method's efficacy. \n\nWeaknesses:\n- The clinical motivation for this paper the authors present seems to be poorly suited for the task and model presented. What would the segmentation of lesions be needed for in a resource-scarce location? Who is taking the images and who is making the decision to biopsy after the segmentation is completed? The authors seem to think a focus on regions with scarce resources and health equity will get their paper more traction, but have not done any of the legwork necessary to ensure their method is applicable, or even interesting, for these situations. This weak motivation distracts from the main contribution of the paper and frankly, makes little sense. The brain MRI images in Figure 1 don't seem to represent a modality which would be available in a resource-scarce location. \n- The authors state that (paraphrased): regions with small ROIs are ignored by existing medical image segmentation methods. This is demonstrably untrue. See https://pmc.ncbi.nlm.nih.gov/articles/PMC9690845/ and https://link.springer.com/article/10.1007/s11432-021-3340-y and https://link.springer.com/article/10.1007/s12652-021-03358-8 for a few examples in breast imaging. \n- The authors Neighborhood Inspection and Difference Prioritization modules seem to be re-deriving traditional radiomic features. Localized variance and mean has been used to describe medical imaging texture for decades. Equation 3 seems to be conceptually similar to a Laplace of a Gaussian feature. \n- BUSI is a low-quality dataset in which about 30% of images contain measurement calipers, providing the model an ROI from which to segment. The authors would be better served testing their method on a cleaner dataset. See Figure 2 for an example lesion with measurement calipers which much be placed by an expert reader such as a sonographer. Which would be unavailable in the authors application area. \n- Again, CT and MRI are not going to be available in a resource-scarce locale. The authors contradict their motivation once again.", "was_revised": true, "final_justification": "The authors have addressed my concerns.", "rebuttal": "We appreciate the reviewer's positive feedback on our solid experiments and the demonstrated effectiveness of NSDA in medical image segmentation tasks. \n\nRegarding the reviewer's concern about our motivation, we respectfully clarify that our focus on digital health equity arises from the inability of lightweight architectures in resource-limited settings to integrate advanced attention mechanisms. These mechanisms currently face an accuracy-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. This limitation prevents equitable access to high-accuracy attention mechanisms in constrained settings, exacerbating digital health divides. By facilitating deployment on edge/mobile/ wearable/portable devices, **our parameter-free NSDA expands accessibility of expert-level digital segmentation in low-resource settings**. Our study aligns with the World Health Organization's **vision for universal health coverage**, responding to one of its core requirements: ensuring equitable access to high-performance digital health technologies within low-resource settings (WHO: Global Strategy on Digital Health 2020-2025).\n\nWe responded to the reviewer's questions point by point.\n\n> W1: The clinical motivation for this paper the authors present seems to be poorly suited for the task and model presented. (i) What would the segmentation of lesions be needed for in a resource-scarce location? (ii) Who is taking the images and who is making the decision to biopsy after the segmentation is completed?  (iii) The authors seem to think a focus on regions with scarce resources and health equity will get their paper more traction, but have not done any of the legwork necessary to ensure their method is applicable, or even interesting, for these situations. This weak motivation distracts from the main contribution of the paper and frankly, makes little sense. (iv) The brain MRI images in Figure 1 don't seem to represent a modality which would be available in a resource-scarce location.\n\nA1: We appreciate the reviewer's critical perspective on our motivation. The reviewers' concerns arose primarily from a misunderstanding of our study's motivation. To clarify, **we mainly focus on low-resource settings/devices rather than resource-scarce location**.\n\n(i) We appreciate the reviewer raising this question regarding the clinical necessity of lesion segmentation in resource-scarce location. Lesion segmentation plays a critical role even in such contexts by providing qualitative and quantitative evidence about lesion size, shape, and location, which supports disease diagnosis and informs radiomic analyses. This information is vital for applications like surgical navigation, evidence-based medicine, and personalized diagnosis. In resource-scarce location, where expert radiologists are scarce, **AI-assisted lesion segmentation can empower primary care providers to better identify and assess anatomical structures and abnormalities in medical images**, helping bridge the gap in diagnostic capacity. Therefore, our work addresses a fundamental need in these underserved areas by making advanced diagnostic capabilities more accessible.\n\n(ii) We appreciate the reviewer's pertinent questions regarding the process of image acquisition and subsequent biopsy decisions. We clarify that **our work focuses exclusively on computational segmentation of medical images**.  Hence, we assume availability of pre-acquired images by low-resource settings like Point-of-Care (POC) Imaging, while **biopsy decisions is a downstream clinical task**. Our segmentation provides quantitative lesion measurements (e.g., shape/size) that assist clinicians in biopsy decisions.\n\n(iii) We appreciate the reviewer's feedback regarding motivation. Our core intention was to establish a technical pathway inspired by radiologists' diagnostic patterns: NSDA enables neural networks to achieve performance gains without computational overhead by modeling local element neighborhood differences through parameter-free design. Critically, **our method enables integration into lightweight architectures, making high-accuracy segmentation feasible in low-resource settings/devices** like portable devices (e.g., Butterfly iQ+) or wearable monitors for point-of-care use in homes, field clinics, or emergencies [1][2][3].\n\n(iv) We appreciate the reviewer's careful observation. We clarify that the images in Figure 2 are cardiac cine-MRI sequences from the ACDC benchmark, not brain MRI. We acknowledge that traditional high-field MRI systems remain limited in resource-scarce regions. We mainly focused on low-resource settings/devices rather than resource-scarce location. **Emerging low-resource settings such as portable and low-field MRI systems (e.g., Hyperfine's AI-powered portable MRI [4]) can take MRI**. This technological progress supports for experiments using MRI data.\n\n> W2: The authors state that (paraphrased): regions with small ROIs are ignored by existing medical image segmentation methods. This is demonstrably untrue. See *ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation. Healthcare (Basel), 2022.*, *Difficulty-aware prior-guided hierarchical network for adaptive segmentation of breast tumors. Sci. China Inf. Sci., 2023.* and *Auxiliary diagnosis of small tumor in mammography based on deep learning. J Ambient Intell Human Comput, 2023.* for a few examples in breast imaging.\n\nA2: We appreciate the reviewer's careful attention, which has helped us identify an imprecision in our wording. The reviewer's concerns stem from the description of a sentence in the Related Work section. This description is in the original text: \"*current methods still process all regions of an image equally during feature learning, failing to pay attention to diagnostically vital ROIs*\". This was intended to refer to the specific methods discussed in the preceding context, not all existing methods broadly. To clarify, **we will revise \"current methods\" to \"the above methods\" in the revised manuscript**, ensuring the statement accurately reflects our focus on the limitations of the approaches cited earlier.\n\n> W3: The authors Neighborhood Inspection and Difference Prioritization modules seem to be re-deriving traditional radiomic features. Localized variance and mean has been used to describe medical imaging texture for decades. Equation 3 seems to be conceptually similar to a Laplace of a Gaussian feature.\n\nA3: We appreciate the reviewer's astute observation linking our work to traditional radiomic features, which provides a valuable opportunity to clarify novelty. While localized mean and variance have long been used in radiomics for handcrafted texture analysis, our Neighborhood Inspection and Difference Prioritization modules integrate these statistics dynamically within a neural network's feature learning process—adapting to hierarchical feature maps and enabling end-to-end optimization, rather than serving as offline, precomputed features. Equation 3, though leveraging mean and variance, is designed to quantify element-neighborhood dissimilarity via a Gaussian kernel complement, guiding neural networks to focus on local salient region. This stands apart from Laplacian of Gaussian features, which focus on edge detection in raw images. **Our method simulates radiologists' Neighborhood Inspection and Difference Prioritization to quantify element-neighborhood discrepancies**, enabling neural networks to focus on salient regions crucial to segmentation.\n\nWe will emphasize these distinctions in the revised manuscript to better highlight the novel integration of these concepts within attention mechanisms.\n\n> W4: BUSI is a low-quality dataset in which about 30% of images contain measurement calipers, providing the model an ROI from which to segment. The authors would be better served testing their method on a cleaner dataset. See Figure 2 for an example lesion with measurement calipers which much be placed by an expert reader such as a sonographer. Which would be unavailable in the authors application area.\n\nA4: We appreciate the reviewer's careful note on the BUSI dataset. We clarify that **measurement calipers occlude tumor regions rather than providing segmentation cues**. As shown in Figure 2, they introduce occlusions that complicate segmentation. Since real-world ultrasound often contains noise (including calipers), BUSI remains a clinically relevant benchmark validated in TMI/CVPR/NPJ Digit. Med. Notably, U-Net struggles with such noise, while NSDA improves accuracy by focusing on genuine lesion discrepancies (Figure 2). As recommended by the reviewer, **we have added experiments on the high-quality TNSCUI2020 ultrasound dataset**.\n\n|Network|mDSC (%)|\n|-|-|\n|TinyU-Net|84.60|\n|+ SE|84.87|\n|+ CBAM|85.02|\n|+ MSCAM|83.96|\n|+ MLKA|84.35|\n|+ NSDA (Ours)|**85.89**|\n\nResults confirm NSDA's effectiveness there as well.\n\n> W5: Again, CT and MRI are not going to be available in a resource-scarce locale. The authors contradict their motivation once again.\n\nA5: We appreciate the reviewer's concern. It's worth noting that recent advancements in Point-of-Care (POC) Imaging have yielded compact, low-resource devices, such as Hyperfine's POC MRI and Samsung's POC CT systems. **These POC devices are specialized for taking CT/MRI in resource-constrained settings**. Our method enables high-accuracy segmentation on resource-constrained emerging devices/settings via integration into neural networks, thereby advancing digital health equity.\n\n---\n\nReference:\n\n[1] \"Unext: Mlp-based rapid medical image segmentation network.\" MICCAI, 2022.\n\n[2] \"Overcoming barriers in the use of artificial intelligence in point of care ultrasound.\" npj Digital Medicine, 2024.\n\n[3] \"See how your body works in real time—wearable ultrasound is on its way.\" Nature, 2024.\n\n[4] \"Low-field MRI: clinical promise and challenges.\" J Magn Reson Imaging, 2023."}, {"review_id": "LRiIBjQAOV", "reviewer": "Reviewer_MPPP", "summary": "This paper proposes a clinically inspired Neighborhood Self-Dissimilarity Attention (NSDA) mechanism, drawing on radiological diagnostic principles to address the long-standing performance–complexity trade-off in attention design through a parameter-free formulation. To further mitigate feature homogenization, the authors introduce a Dynamic Neighborhood Scaling (DyNS) strategy.", "questions": "(1) In Fig. 3, the effectiveness of DyNS is demonstrated only on the Synapse dataset. However, its performance under small-object or densely structured targets remains untested, limiting conclusions about the generalizability and robustness of the DyNS design.\n\n(2) The neighborhood size in Equation (1) is heuristically determined using a fixed scaling factor (S = 8) and a compensatory constant (c), but the paper lacks sensitivity analysis to evaluate how this setting behaves across diverse network hierarchies and segmentation scenarios.\n\n(3) Although NSDA is described as “parameter-free,” the implementation involves per-pixel computation of local mean, variance, and exponential transformations, which introduce non-trivial computational cost and implicit hyperparameters. The paper does not provide an analysis of the resulting resource consumption or inference latency.\n\n(4) While Fig. 3 demonstrates the advantage of replacing static neighborhood sizes with DyNS, the paper does not report the performance of NSDA when DyNS is entirely removed, leaving the necessity of this component only partially evaluated.", "limitations": "See weaknesses and questions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n(1) This paper presents a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA) framework that enables neural networks to focus on element-neighborhood differences, thereby boosting segmentation accuracy.\n(2) This paper introduces a Dynamic Neighborhood Scaling (DyNS) strategy in NSDA, which adaptively regulates NSDA’s neighborhood size across network hierarchies to prevent feature homogenization.\n(3) Experimental results show the effectiveness of the proposed model.\n\nWeaknesses:\n(1) While medical image segmentation typically relies on shape priors, multi-scale context, and boundary cues, NSDA instead emphasizes element-to-neighborhood dissimilarity. The authors do not sufficiently explain why such a “self-dissimilarity” mechanism offers better discriminative power than direct features, nor do they clarify its rationality and advantages in structured scenarios such as connected anatomical regions.\n(2) Although the authors claim to present the first parameter-free attention mechanism based on element–neighborhood dissimilarity, similar ideas have appeared in prior works such as SimAM and ULSAM, which leverage local statistics (e.g., mean, variance) or subspace reconstruction errors to generate attention weights. The paper does not adequately distinguish NSDA from these approaches, either conceptually or theoretically.\n(3) The dissimilarity computation is based on a fixed Gaussian kernel, rather than a learnable or adaptive metric. The authors do not provide justification for the selection or regulation of the variance term, which may lead to either over-smoothing or noise amplification. It also remains unclear why this fixed-kernel approach is preferable to alternative similarity or distance measures for segmentation.\n(4) The multi-scale attention fusion in NSDA lacks detail on how weights across scales are determined or balanced. Moreover, the absence of ablation experiments regarding fusion strategies weakens the empirical support for this design component.", "was_revised": true, "final_justification": "The authors have provide rebuttal to address the previous concerns, and several concerns have been addressed. Thus, I raise the score.", "rebuttal": "We thank the reviewer for affirming the merits of our paper, particularly its clear presentation and effective methodology. We address your questions as follows.\n\n> W1: While medical image segmentation typically relies on shape priors, multi-scale context, and boundary cues, NSDA instead emphasizes element-to-neighborhood dissimilarity. The authors do not sufficiently explain why such a “self-dissimilarity” mechanism offers better discriminative power than direct features, nor do they clarify its rationality and advantages in structured scenarios such as connected anatomical regions.\n\nA1: We address reviewer's concerns by clarifying the rationality and advantages of our approach. NSDA can identify boundary information by leveraging element-neighborhood dissimilarity. It enhances segmentation completeness near edges for structures like the pancreas, right ventricle, and benign breast tumors by guiding U-Net to focus on local regions (Figure 2). This is because elements at boundaries exhibit greater dissimilarity to their surrounding neighborhoods than those in interior regions. Hence, **NSDA amplifies discriminative signals in local regions by prioritizing zones of high dissimilarity**. Further, the quantitative results (Tables 1 and 2) and qualitative results (Figure 2) demonstrate the effectiveness of our approach.\n\n> W2: Although the authors claim to present the first parameter-free attention mechanism based on element–neighborhood dissimilarity, similar ideas have appeared in prior works such as SimAM and ULSAM, which leverage local statistics (e.g., mean, variance) or subspace reconstruction errors to generate attention weights. The paper does not adequately distinguish NSDA from these approaches, either conceptually or theoretically. \n\nA2: We clarify the distinctive aspects of NSDA compared to existing methods (e.g., SimAM, ULSAM), thus addressing reviewer's misconception regarding its conceptual and theoretical foundations. ULSAM explicitly employs 1×1 group convolutions to generate attention weights, introducing trainable parameters. Both SimAM and ULSAM generate attention weights based on global feature statistics. Specifically, **Our NSDA has the three main innovations**.\n- *Adaptive Localization*: Per-layer adaptive adjustment of neighborhood size (Equation 1) to prevent preventing feature homogenization from excessive long-range aggregation.\n- *Novel Dissimilarity Measure*: First Gaussian-kernel-based metric leveraging local mean/variance (Equation 3) for element-neighborhood comparisons, providing theoretical justification for attention weighting.\n- *Radiologist-inspired Parameter-Free Paradigm*: Unlike the traditional paradigm (Convolution/MLP layers combined with Sigmoid/Softmax), NSDA introduces a parameter-free architecture, simulating radiologists' Neighborhood Inspection and Difference Prioritization, offering new design insights for attention mechanisms.\n\n> W3: The dissimilarity computation is based on a fixed Gaussian kernel, rather than a learnable or adaptive metric. The authors do not provide justification for the selection or regulation of the variance term, which may lead to either over-smoothing or noise amplification. It also remains unclear why this fixed-kernel approach is preferable to alternative similarity or distance measures for segmentation.\n\nA3: To address reviewer's concerns, we clarify: (i) the rationale for our fixed Gaussian kernel and variance selection, and (ii) NSDA's advantages over alternative similarity or distance measures for segmentation. **Fixed dissimilarity measure ensures consistent cross-sample comparability** by preserving classical ML principles (e.g., SVM/PCA kernels), whereas learnable measures undermine fairness due to input-dependent variations. Our variance term (Equation 3) aligns with classical machine learning method (e.g., SVM and PCA). **We conduct a sensitivity analysis for the variance term** on the Synapse dataset and report mean DSC (%).\n\n|Network|$\\sigma_{i,j}^2$|$2\\sigma_{i,j}^2$ (Ours)|$4\\sigma_{i,j}^2$|$6\\sigma_{i,j}^2$|\n|-|-|-|-|-|\n|NSDA-augmented U-Net|80.52|**81.62**|79.33|79.05|\n|NSDA-augmented TinyU-Net|80.34|**81.57**|80.06|80.18|\n\nResults show our setting achieves optimal segmentation accuracy. This can be attributed that a larger variance term reduces sensitivity to feature differences, whereas a smaller one amplifies it.\n\nNSDA outperforms other similarity or distance metrics (Table 3) due to:\n- *Clinical relevance*: Lesions/organs exhibit high dissimilarity from surroundings, aligning with radiologists' focus on discrepancy regions;\n- *Technical superiority*: Unlike alternatives such as Euclidean distance requiring sigmoid/softmax to generate attention weights, our Gaussian-based measure provides inherent nonlinearity while eliminating sign-induced bias (Figure 1), ensuring fair contribution from opposing-polarity features.\n\nWe will enhance the paper's corresponding descriptions to improve clarity\n\n> W4: The multi-scale attention fusion in NSDA lacks detail on how weights across scales are determined or balanced. Moreover, the absence of ablation experiments regarding fusion strategies weakens the empirical support for this design component.\n\nA4: The reviewer's concerns stem from a misunderstanding of our work. We clarify that **our NSDA does not incorporate multi-scale attention fusion**. Nevertheless, reviewer's insight has prompted plans to develop a multi-scale variant in future work.\n\n> Q1: In Fig. 3, the effectiveness of DyNS is demonstrated only on the Synapse dataset. However, its performance under small-object or densely structured targets remains untested, limiting conclusions about the generalizability and robustness of the DyNS design.\n\nA5: To solve reviewer's concerns, we clarify that the Synapse benchmark (widely adopted in top venues like CVPR/NeurIPS) inherently includes small objects (e.g., aorta/pancreas, Figure 2). **We additionally validated performance on the COVID-19 CT dataset (Cell, 2020)**, reporting DSC (%) for clinically significant small consolidations. This dataset is from ''Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography''\n\n|Network|Small Organ/Lesion|DyNS (Ours)|K=(03,03)|K=(07,07)|K=(15,15)|K=(31,31)|K=(41,41)|K=(51,51)|K=(H,W)|\n|-|-|-|-|-|-|-|-|-|-|\n|NSDA-augmented TransUNet|Pancreas|**63.66**|63.09|63.65|62.87|62.55|61.22|61.31|61.33|\n|NSDA-augmented TinyU-Net|Aorta|**88.81**|87.07|87.43|87.99|88.68|88.06|87.91|87.74|\n|NSDA-augmented UNet|Consolidation|**79.74**|79.38|79.13|79.33|79.42|79.40|78.52|78.18|\n\nResults demonstrate DyNS’s robustness across small and dense anatomical structures. \n\n> Q2: The neighborhood size in Equation (1) is heuristically determined using a fixed scaling factor (S = 8) and a compensatory constant (c), but the paper lacks sensitivity analysis to evaluate how this setting behaves across diverse network hierarchies and segmentation scenarios.\n\nA6: To solve reviewer's concerns, we clarify that the compensatory constant $c \\in {0,1}$ is used for ensuring element’s bilateral symmetry within neighborhoods. **We conducted sensitivity analysis** for scaling factor $S$ on Synapse dataset , reporting mean DSC (%).\n\n|Network|S=2|S=4|S=8 (Ours)|S=16|S=32|\n|-|-|-|-|-|-|\n|NSDA-augmented U-Net|80.68$\\pm$0.80|79.96$\\pm$0.55|**81.35$\\pm$0.52**|79.74$\\pm$0.64|78.94$\\pm$0.83|\n|NSDA-augmented TinyU-Net|80.54$\\pm$0.69|79.37$\\pm$0.82|**81.09$\\pm$0.46**|79.51$\\pm$0.74|80.29$\\pm$0.55|\n\nOptimal performance occurs at $S=8$, as smaller $S$ expands receptive fields but diluting local features, while larger $S$ limits contextual capture.\n\n> Q3: Although NSDA is described as “parameter-free,” the implementation involves per-pixel computation of local mean, variance, and exponential transformations, which introduce non-trivial computational cost and implicit hyperparameters. The paper does not provide an analysis of the resulting resource consumption or inference latency.\n\nA7: To solve reviewer's concerns, we clarify that our \"parameter-free\" claim refers exclusively to the absence of learnable parameters (e.g., convolution/MLP weights) that increase model size and require gradient updates. **While using statistical operations (e.g., mean/variance), these leverage highly optimized PyTorch tensor primitives with substantially lower cost** than parametric operations (e.g., convolutions/matrix multiplications), which are the the primary FLOPs/MACs contributors in deep networks. Thus, the pytorch implementation of NSDA is efficient and lightweight. The code is available upon publication.\n\nAs shown in Table 1, NSDA maintains near-identical parameter counts and FLOPs/MACs versus base networks (e.g., 19.58M params in NSDA-augmented U-Net matching original U-Net), while achieving higher throughput than most baselines (e.g., 105.37 FPS for NSDA-augmented U-Net vs. 82.78 FPS for CBAM-augmented U-Net). Results confirm NSDA introduces negligible computational overhead and causes no significant inference delays.\n\nWe will implement the suggested analyses and clarifications during revision to strengthen claims.\n\n> Q4: While Fig. 3 demonstrates the advantage of replacing static neighborhood sizes with DyNS, the paper does not report the performance of NSDA when DyNS is entirely removed, leaving the necessity of this component only partially evaluated.\n\nA8: The reviewers' concerns mainly stem from misunderstandings of our work. Complete removal of DyNS would render neighborhood size $b_L$ (Equation 1) undefined, necessitating manual setting of a static size $K$. Only when a static neighborhood size $K$ is established can the element-neighborhood dissimilarity measure (Equation 2) remain mathematically defined; otherwise, **removal of DyNS renders this measure inoperable**.\n\nWe will clarify this architectural necessity in the paper to prevent misunderstandings and enhance overall clarity."}, {"review_id": "twFhrZ1D4z", "reviewer": "Reviewer_xBWV", "summary": "Authors propose a novel Neighborhood Dissimilarity Attention mechanism to aid medical image segmentation in resource-limited settings. Based on the way radiologists inspect neighboring pixels or elements in an image, Authors propose a weighting of spatial features based on the level of dissimilarity between each pixel and those of its neighborhood. Dynamic Neighborhood Scaling is introduced to limit the neighborhood at subsequent model depths, to avoid feature homogenization. Attention Fusion is also used, whereby features are scaled by the attention weights and an added to the original features via a residual - this avoid additional complexity usually added with an MLP. Authors demonstrate their method in 3 distinct imaging domains - CT, MRI, and Ultrasound - as well as natural images. Results demonstrate that NDSA achieves most significant performance gains under test, and is able to perform well across architectures and domains. The paper shows that dissimilarity-based attention yields better performance than similarity based attention.", "questions": "In eq1, `S` is empirically set to 8 to help ensure the neighborhood of attention remains proportional to feature map scales across network depths. Could this be done more algorithmically using some aggregation of convolutional kernel size and stride etc.?", "limitations": "Authors have not listed any limitations of their method in their Limitations section - instead, they have only noted that resource-limited setting need lower complexity approaches, which is what they address in their work. Authors should use this section to note actual limitations of their approach.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The authors set the context of their research well, and lay out a measured approach to understand how their proposed NDSA can enhance the performance of attention mechanisms in the low-resource setting.\n\nIn addition, Authors present a solid set of RQs, and demonstrate their approach on a diverse domains. Authors provide additional investigation by extending their analysis to the natural domain to assess generalization of their method. It is appreciated that Authors also compare against available resource-limited variants of attention mechanisms, given these are their main alternatives. Authors use appropriate metrics for analysis.\n\nAuthors have focused on the UNet architecture and its variants in this paper due to the resource-limited nature of their focus - it is known that alternative approaches such as transformer-based architectures can yield impressive results in image segmentation tasks. It would be beneficial for authors to compare their approach to other, more traditional attention mechanisms too.", "was_revised": true, "final_justification": "Based on Authors' rebuttal and discussion on these and other Reviewers' comments, I maintain the rating of 5: Accept.", "rebuttal": "We deeply appreciate your thoughtful review, especially your endorsement of our writing clarity, research context, methodological rigor, solid experiments, targeted benchmarking, and cross-domain validation. This affirmation motivates our continued commitment to thorough research, and we address your questions below.\n\n> W1: Authors have focused on the UNet architecture and its variants in this paper due to the resource-limited nature of their focus - it is known that alternative approaches such as transformer-based architectures can yield impressive results in image segmentation tasks. It would be beneficial for authors to compare their approach to other, more traditional attention mechanisms too.\n\nA1: Thank you for your valuable suggestion. While our initial comparisons included transformer-based U-Net variants like TransUNet (Table 1), we appreciate your point about network architectures. In addition, the most classic attention mechanisms are SE and CBAM, which are widely used in various computational vision fields such as image classification, object detection, and semantic segmentation.  **We accept your suggestion to perform new experiments** with transformer-based SegFormer (NeurIPS’21) and the classic lightweight attention mechanism ULSAM on Synapse dataset. \n\n|Network|Params (M)|FLOPs (G)|Throughput (FPS)|mDSC (%)|\n|-|-|-|-|-|\n|SegFormer|3.716|3.399|134.59|68.91|\n|+ SE|3.728|3.399|129.26|69.92|\n|+ CBAM|3.729|3.401|134.59|69.88|\n|+ MSCAM|4.155|3.608|134.59|67.14|\n|+ ULSAM|3.719|3.403|134.59|69.43|\n|+ NSDA (Ours)|3.716|3.399|119.70|**71.40**|\n\nResults further demonstrate NSDA’s effectiveness even on transformer-based architectures.\n\n> Q1: In eq1, $S$ is empirically set to 8 to help ensure the neighborhood of attention remains proportional to feature map scales across network depths. Could this be done more algorithmically using some aggregation of convolutional kernel size and stride etc.?\n\nA2: Thank you for this insightful observation. You are absolutely right. DyNS is currently implemented by dividing the feature map size by $S$ (Equation 1). For computing statistics like an average $y_{i,j}$ in Equation 2, we use pooling kernel size of ($b_L\\times b_L$) (Equation  1)  for efficient calculation. \n\nWe appreciate your attention to this detail. **The PyTorch code will be released upon publication**.\n\n> L1: Authors have not listed any limitations of their method in their Limitations section - instead, they have only noted that resource-limited setting need lower complexity approaches, which is what they address in their work. Authors should use this section to note actual limitations of their approach.\n\nA3: Thank you very much for your insightful suggestion on the Limitations section. **We agree with you and illustrate the practical limitations of our approach**. Our method operates on the spatial dimensions of feature maps, without taking the channel dimension of feature maps into account. Recent studies have indicated that there is redundancy in the channels of feature maps. How to prioritize the channels that are important to network performance in NSDA using a parameter-free method will be our future work. This is expected to provide insights for the model's structure optimization and resource allocation. \n\nWe will revise and improve our Limitations section accordingly. Thank you for your positive feedback and constructive suggestions on our paper."}, {"review_id": "SusjTFxct2", "reviewer": "Reviewer_WMJY", "summary": "This paper proposes a novel, parameter-free attention mechanism called Neighborhood Self-Dissimilarity Attention (NSDA) for medical image segmentation. The core idea is inspired by radiologists' diagnostic process, where they focus on regions that exhibit high contrast or difference from their immediate surroundings. Instead of computing pairwise similarity like in conventional self-attention, NSDA calculates an attention score for each feature element based on its dissimilarity to the aggregated representation of its local neighborhood. The dissimilarity is quantified using a complement of a Gaussian kernel. The method also introduces a Dynamic Neighborhood Scaling (DyNS) strategy to adapt the size of the local neighborhood across different layers of the network, preventing feature homogenization. The authors claim this approach improves segmentation accuracy without adding computational complexity (in terms of parameters and MACs), thus addressing the performance-complexity trade-off and promoting health equity in resource-limited settings.", "questions": "1. The performance gains reported in Table 1 are impressive. Could the authors clarify if these results are from a single run or averaged over multiple runs? To strengthen the paper's claims, I would strongly recommend that the authors run the experiments for the main backbones (e.g., U-Net and TinyU-Net) with at least 3-5 different random seeds and report the mean and standard deviation for the Dice scores. This would provide crucial evidence of the statistical significance of the improvements over baselines. An update with this information would likely increase my overall score. \n\n2. The DyNS strategy uses a fixed scale factor S=8. The paper would benefit from a discussion on how this value was chosen. Could the authors provide a brief sensitivity analysis showing how performance (e.g., mean DSC on Synapse) varies with different values of S? This would help to better understand the robustness of this heuristic. \n\n3. The generalization experiments in Table 2 show that NSDA performs well on the natural image dataset VOCSeg. This suggests that \"dissimilarity\" might be a more general principle for attention, extending beyond its motivation as a \"clinical prior\". Could the authors elaborate on why they believe this principle generalizes so effectively outside of medical imaging, where the \"lesion vs. background\" intuition might be less directly applicable?", "limitations": "Yes. The authors have adequately addressed the limitations in a dedicated paragraph. They correctly point out that while their attention module is lightweight, the overall deployment feasibility still depends on the size of the backbone network architecture, and they suggest future work on lightweight networks. This is a fair and honest assessment.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1. The core idea of a parameter-free, dissimilarity-based attention mechanism is highly original and significant. It is well-motivated by clinical practice and directly addresses the critical performance-complexity trade-off, making it highly practical for resource-constrained applications. \n\n2. The method is tested on multiple backbones and datasets against a wide array of baselines. Furthermore, strong ablation studies clearly demonstrate the contribution of each proposed component (DyNS and the dissimilarity measure). \n\n3. The paper is very well-written, clearly structured, and easy to follow, which significantly enhances the accessibility of its contributions. \n\nWeaknesses: \n\n1. The performance gains reported in Table 1 are impressive. Could the authors clarify if these results are from a single run or averaged over multiple runs? To strengthen the paper's claims, I would strongly recommend that the authors run the experiments for the main backbones (e.g., U-Net and TinyU-Net) with at least 3-5 different random seeds and report the mean and standard deviation for the Dice scores. This would provide crucial evidence of the statistical significance of the improvements over baselines. An update with this information would likely increase my overall score. \n\n2. The DyNS strategy uses a fixed scale factor S=8. The paper would benefit from a discussion on how this value was chosen. Could the authors provide a brief sensitivity analysis showing how performance (e.g., mean DSC on Synapse) varies with different values of S? This would help to better understand the robustness of this heuristic. \n\n3. The generalization experiments in Table 2 show that NSDA performs well on the natural image dataset VOCSeg. This suggests that \"dissimilarity\" might be a more general principle for attention, extending beyond its motivation as a \"clinical prior\". Could the authors elaborate on why they believe this principle generalizes so effectively outside of medical imaging, where the \"lesion vs. background\" intuition might be less directly applicable? \n\n4. The experiments integrate the attention module only at the terminal layer of the feature extractor. In U-Net like architectures, attention is often applied at multiple stages of the encoder and decoder. An investigation into how the performance of NSDA changes with different placement strategies would have provided deeper insights into its mechanism.", "was_revised": true, "final_justification": "The author resolved the majority of the issues.", "rebuttal": "We deeply appreciate your professional reviews and constructive suggestions. Your comments are excellent and thank you for your approval of our study. We are delighted by your praise for the originality and significance of our methodology. In addition, we are pleased that you have praised our paper for being well written, clearly structured and easy to understand. We answer the questions as follows.\n\n> W1/Q1: The performance gains reported in Table 1 are impressive.  Could the authors clarify if these results are from a single run or averaged over multiple runs?  To strengthen the paper's claims, I would strongly recommend that the authors run the experiments for the main backbones (e.g., U-Net and TinyU-Net) with at least 3-5 different random seeds and report the mean and standard deviation for the Dice scores.  This would provide crucial evidence of the statistical significance of the improvements over baselines.  An update with this information would likely increase my overall score.\n\nA1: We deeply appreciate your constructive suggestions to strengthen the paper's claims. **Table 1 reports peak performance from a single run**, consistent with common practice in publications like TMI, CVPR and NeurIPS.  We appreciate and value your insight. However, generating comprehensive mean and standard deviation significantly increases computational costs due to rerunning all experiments multiple times, which is challenging given our lab's GPU constraints and the rebuttal timeframe.  Nonetheless, **we have made every effort to supplement experiments and reported mean DSC (%)**: we prioritized five recent advanced attention methods and conducted experiments with three different random seeds on the challenging Synapses dataset using main backbones (U-Net and TinyU-Net). \n\n|Attention|Source|U-Net|TinyU-Net|\n|-|:-:|:-:|:-:|\n|||77.78$\\pm$0.55|78.12$\\pm$0.59|\n|+ CGA|TIP'24|77.11$\\pm$0.89|79.20$\\pm$0.77|\n|+ CMO|TPAMI'24|76.45$\\pm$0.67|79.11$\\pm$0.51|\n|+ MLKA|CVPR'24|78.71$\\pm$0.53|80.09$\\pm$0.66|\n|+ MSCAM|CVPR'24|75.92$\\pm$1.12|44.59$\\pm$6.35|\n|+ MWSA|AAAI'25|47.36$\\pm$4.15|72.71$\\pm$0.64|\n|+ NSDA|Ours|**81.35$\\pm$0.52**|**81.09$\\pm$0.46**|\n\nThe updated results (avg ± std) consistently confirm our method's superiority. We sincerely appreciate your constructive feedback and the opportunity to enhance our work. We will continue to conduct experiments to provide statistically significant evidences/results.\n\n> W2/Q2: The DyNS strategy uses a fixed scale factor S=8.  The paper would benefit from a discussion on how this value was chosen.  Could the authors provide a brief sensitivity analysis showing how performance (e.g., mean DSC on Synapse) varies with different values of S?  This would help to better understand the robustness of this heuristic.\n\nA2: Thank you for this insightful suggestion. **We conduct a sensitivity analysis of scale factor $S$** on the Synapse dataset and report mean DSC (%).\n\n|Network|S=2|S=4|S=8 (Ours)|S=16|S=32|\n|-|-|-|-|-|-|\n|NSDA-augmented U-Net|80.68$\\pm$0.80|79.96$\\pm$0.55|**81.35$\\pm$0.52**|79.74$\\pm$0.64|78.94$\\pm$0.83|\n|NSDA-augmented TinyU-Net|80.54$\\pm$0.69|79.37$\\pm$0.82|**81.09$\\pm$0.46**|79.51$\\pm$0.74|80.29$\\pm$0.55|\n\nWe find that the optimal performance at $S=8$. We hypothesize that smaller $S$ expands fields but risks diluting local features, while larger $S$ excessively constrains contextual capture. This trade-off explains the robustness of $S=8$.\n\nThank you again for your careful review. We will optimize the description with respect to $S$ for better understand.\n\n> W3/Q3: The generalization experiments in Table 2 show that NSDA performs well on the natural image dataset VOCSeg.  This suggests that \"dissimilarity\" might be a more general principle for attention, extending beyond its motivation as a \"clinical prior\".  Could the authors elaborate on why they believe this principle generalizes so effectively outside of medical imaging, where the \"lesion vs. background\" intuition might be less directly applicable?\n\nA3: Thanks for your thoughtful observation regarding our generalization to natural image benchmark VOCSeg, which helps us clarify the broader applicability for our approach. While our method was initially inspired by the clinical prior of lesion-background dissimilarity in medical imaging, **the underlying \"dissimilarity\" principle extends beyond this specific context** because it captures a universal property of visual information: meaningful regions, whether lesions in medical images or objects in natural scenes, are inherently distinguishable from their surroundings through distinct feature patterns. In medical imaging, this manifests as the contrast between pathological and normal tissues, but in natural images like VOCSeg, it translates to differences in texture, color, or structure that define object boundaries or semantic entities. The clinical prior served as a concrete motivation, but the core mechanism relies on detecting such intrinsic feature discrepancies, a cue that remains critical for segmentation tasks across domains even when the \"lesion vs. background\" intuition is less direct. \n\nWe appreciate your observation and will elaborate on this connection between the clinical inspiration and the principle’s generalizability in the revised manuscript.\n\n> W4: The experiments integrate the attention module only at the terminal layer of the feature extractor.  In U-Net like architectures, attention is often applied at multiple stages of the encoder and decoder.  An investigation into how the performance of NSDA changes with different placement strategies would have provided deeper insights into its mechanism.\n\nA4: Thank you for your thoughtful feedback. We clarify that **a feature extractor is deployed as an independent component at every stage of both the encoder and decoder**, and the attention is applied to each extractor's output. We will refine this description in the final version of the paper. \n\nWhile our primary focus is architectural innovation rather than placement strategy in the attention mechanism, we appreciate your perspective. Accordingly, **we conducted a brief ablation study regarding attention’s placement** using U-Net on the Synapse dataset. \n\n|NSDA-augmented Encoder|NSDA-augmented Decoder|mDSC (%)|\n|-|-|-|\n|||77.78$\\pm$0.55|\n|$\\checkmark$||79.56$\\pm$0.78|\n||$\\checkmark$|79.23$\\pm$0.46|\n|$\\checkmark$|$\\checkmark$|**81.35$\\pm$0.52**|\n\nResults indicate that applying NSDA to both encoder and decoder stages outperforms using it solely in either the encoder or decoder, maximizing its effectiveness."}]

Author rebuttal:

Meta-review: This paper presents a novel Neighborhood Self-Dissimilarity Attention for medical image segmentation. The authors validate the effectiveness and generality of the proposed attention with four segmentation baseline, including Unet, TransUnet, UNeXt, and TinyU-Net. The experimental results consistently show the advantages of the NSDA module. Overall, the reviewers are supportive to this paper.
Decision: Accept (spotlight)