Towards Text-Mask Consistency in Medical Image Segmentation
Authors: Jie Gui
HangTu
Wen Sha
Xiuquan Du
Venue: ICLR 2026 Poster (2026)
Keywords: Medical image segmentation
Vision language models
Multimodal learning
Kolmogorov–Arnold Networks
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Vision-language models for medical image segmentation often produce masks that conflict with the accompanying text, especially under multi-site/multi-lesion descriptions. We trace this failure to two factors: (i) highly templated and repetitive clinical language causes one-to-one hard contrastive learning to yield numerous false negatives, weakening cross-modal alignment; and (ii) predominantly vision-driven, one-way cross-attention lacks a language-dominant, spatially aware pathway, hindering effective injection of textual semantics into the spatial visual domain. To this end, we propose Consistency-enhanced Two-stage Segmentation (C2Seg). In the pretraining stage, Cluster-aware Contrastive Learning uses a frozen strong baseline to construct an intra-batch text similarity matrix as soft labels, thereby alleviating false negative conflicts and producing more discriminative visual representations. In the fusion stage, we introduce a Bidirectional Complementary Attention Module, where each modality dominates attention along its own path, fostering deep interaction and structural consistency between visual and textual representations. In order to enhance the expressive power of multimodal features, we further adopt KAN-based Attention Gating. Without updating the language encoder, our approach significantly improves text-mask consistency and segmentation accuracy on four public medical imaging datasets.

Published as a conference paper at ICLR 2026
TOWARDS TEXT-MASK CONSISTENCY IN MEDICAL IMAGE SEGMENTATION
Jie Gui Hang Tu², Wen Shal.2;
Xiuquun Du
Laborrtoryy of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China
2 School of Artificial Intelligence, Anhui University, Hefei, China
Schooo of Computer Science and Technology, Anhui University, Hefei, China fw12511 1332, w1251220009)e0 ahu. edu.cn
[04069, dxl1l ahu. edu.cn
ABSTRACT
Vision-language models for medical image segmentation often produce masks that conffict with the accompanying text, especially under multi-sine/multi-lesionn de- scriptions. We trace this failure two factors: (i) highly templated and repet- itive clinical language causes one-to-one hard contrastive learning to yield nu- merous false negatives, weakening cross-modes moosl alignment; and (ii) predominantly vision-driven, one-way cross- attosnttt lacks a Language-dominant spatially aware pathway, hindering effective injection of textual semanties into the spatial visual Two-stage Segmenta-
domain. To this end, we propose Consistency-enhante tion (C2Seg). In the pretraining stage, Cluster-aware Contrastive Learning uses frozen strong baseline to construct an intra-batch text similarity matrix soft labels, thereby alleviating false negative conflicts and producing more discrimina- tive visual representations. In the fusion stage, we introduce Bidirectional Com- plementary Attention Module, where each modality dominates attention along its own path, fostering deep interaction and structural consistency between visual and textual representations. In order to enhance the expressive power of multi- modal features, further adopt KAN-based Attention Gating. Without updating the language encoder, our approach significantly improves text-mask consistency and segmentation accuracy on four public medical imaging datasets.
INTRODUCTION
Despite the remarkable progress of vision-language models (VLMs) in visual understanding through large-scale image-text alignment (Ghosh et al. [2024] Li et al.) 2025b), current medical VLMs still frequently produce masks that contradict the accompanying text on key semantic attributes, espe- cially in multi-site/multi-lesss specifies quantity and spatial cues such as "Bilateral", "two", or "upper left", the predicted masks may still fail to match the described number of lesions their coarse locations. This phenomenon suggests that existing pipelines have not yet effectively transformed clinical language into pixel-level structural constraints, making it difficult to ensure tributes such as lesion count, laterality, and coarse spatial position.
scenarios. As illustrated in Fig. I a). even when the text explicitly
consistency at the level of semantic at-
We trace this mismatch to two underlying factors, illustrated in Fig. [Tb.. Firstly, clinical de- scriptions are highly templated and semantically repetitive, so the same short phrase can correspond to different imaging instances. In QaTaa-COV19 dataset, for example, roughly 7,000 cases share only about 300 unique text templates, which means that the same text is frequently reused within the same training mini-batch rather than being rare coincidence. Under this distribution, main- stream InfoNCE-style contrastive learning (Hu et al. 2024) still enforces a strict one-to-one match- ing, treating each image-text pair (7(), T()) as the only positive and all unpaired combinations + as negatives. In medical settings, however, T() and some TU) can be exactly the same template (e.g., "unilateral pulmonary infection, one infected area"), "), so pairs like (1(), T())
"Corresponding author.

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Published as a conference paper at ICLR 2026
(o) Challenge: Inconsistency between text and mask.
(6) Limitations: (i] controstive confilets
(2) unidirectonnl fusion
image' Ground
Meak
Qvensity
duaaa
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Interton Led
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[A) Cluster aware Contrasthvl Leorniigg.
(2) distrcctional Complementrry Afrendion
Toul
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Figure 1: Challenge and limitations of existing methods, and our solution.
are incorrectly pushed apart as strong negatives. This hard-coding of nearly identical semantics as negatives produces a large number of false negatives and contrastive conflicts, ultimately degrad- ing cross-modal alignment quality, Secondly, most existing methods still rely on vision-centrie, unidirectional cross-attention mechanisms (Wang al. 2024 Chen al. 2025a). Although some works explore Dual-path attention (DualA) between vision and language in their fusion architec- tures (Huang al. 2025), the textual features in these approaches typically only modulate visual features indirectly through attention weights, without forming an explicit, language-doming spatial representation. As result, such methods remain essentially vision-centric, and their exploitation and modeling of linguistic semantics are still insufficient.
To this end, propose Consistency-enhancee Two-stage Segmentation framework (C2Seg). As illustrated in Fig. [.]. Stage I employs Cluster-aware Contrastive Learning (CaCL), which lever- ages implicit batch-wise semantic neighborhoods in frozen language space construct soft label distributions. This allows the image-tex, contrastive loss to fit continuous semantic similarity dis- tribution rather than hard positives/negatives, thereby alleviating contrastive conflicts and producing more discriminative, robustly aligned visual representations. In Stage II, we design a Bidirectional Complementary Attention Module (BCAM) that augments the traditional vision-dominan cross- attention with a language-dominant path while preserving spatial structure: the vision path outputs "each pixel enhanced by sentence-level semantics," whereas the language path captures "the ag- gregated per-token influence at each pixel," enabling spatially aware, deep bidirectional interaction. Furthermore, we introduce a KAN-based Attention Gating (K-Gate) that adaptively weights spatial locations and -specifc features for fine- ane-grained feature selection; KANs also applied in the visual encoder and BCAM to provide nonlinear modeling capacity with limited parameter overhead. The contributions of this work can be summarized as follows:
We propose CaCL that converts inter-text similarity into soft labels for contrastive learning, effectively suppressing false-negative conflicts, strengthening cross-modal alignment, and promoting semantic consistency.
We design BCAM, which consists of two parallel vision-dominant and paths, enabling deep bidirectional interaction and complementary enhancement while pre- serving spatial structure.
We introduce K-Gate to perform nonlinear modeling of visual and language features sepa- rately through KANs, thereby achieving fine- fine-grained selection of tross-modal information.
We present C2Seg and demonstrate its significant improvements on text-mask consistency and segmentation accuracy through extensive experiments on four public medical datasets.
2
RELATED WORK
Vision Language Models. In recent years, the success of general vision-language pretraining models has driven multimodal research in the medical domain. For example, Zhang et al. (2025h)
2

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performs image-text contrastive pretraining large-scale biomedical figures and reports, learn- ing generic medical image-text representations for retrieval and classification; Li et al. (2023) ex- VLMs to medical visual question answering and dialogue scenarios. Meanwhile, [Cheng et al.] (2023) and Ma et al. (2024) transfer the Segment Anything paradigm to medical imaging, achieving strong organ and lesion segmentation performance under prompts. Leveraging the rich multimodal semantic representations and scalable model interfaces offered by these foundation models, number of vision-language guided segmentation methods have recently emerged Tomar et al. [022] Huemann et al. [0244] Li et al. 2024] Wang et al. 2025b 2026). How- ever, despite the diversity of fusion mechanisms, most existing multimodal segmentation models still rely on a unidirectional path where language guides vision, and lack explicit modeling of linguistic semantics. Although Liu et al. (202);; : Cho et al. (024); /Sultan et al. (2025) construct bidirectional interaction structures between vision and language, their so-called "I manguage-dominann attention branches typically output only updated text tokens, while the final segmentation prediction still de- pends on feature maps from the visual branch. In other words, text mainly modulates visual features indirectly through attention weights, without forming an explicit language-centre spatial represen- tation, which limits the modeling of fine-grained, text-conditioned spatial details.
tends instruction-followiii
Contrastive Learning. Multimodal pretraining networks utilize large-scale image-text pairs for contrastive learning to achieve effective cross-modal alignment (Mukhoti et al.) 2023] Chng et al.) 2024| Sung al. 2024). In the context of medical image, the high cost of acquiring medical data and pixel-level annotations makes the incorporation of richly paired medical text and images a nat- ural solution (Wang al., 2022. Hu et al. 2023). Contrastive learning methods can align medical text and images, providing crucial semantic guidance for the segmentation process (Pan et al. 2025). However, contrastive objectives are known to suffer from false negatives and class collisions, and this issue is further exacerbated in clinical practice where medical reports are highly templated and heavily reused: the same or nearly identical wording can appear in many different cases, tra- ditional hard sampling often mislabels semantically similar pairs as negatives, compromising the stability and accuracy of the alignment. To mitigate such effects, Li et al. (202I) introduce clus- ter prototypes to encode semantic structure, while Dwibedi et al. (2021) augments positives with nearest neighbors in the feature space. Nonetheless, even when the contrastive objective is relaxed from strictly one-to-one to one-to-many, these methods still rely on hard positive/negative assign- ments at the label level, making difficult to explicitly model the continuous semantic similarity distribution between samples and thus preventing them from effectively mitigating the soft-positive problem associated with semantic neighbors.
Kolmogorov-Armold Networks. The Kolmogorov-Amold representation theorem states that any multivariate continuous function f(11. Z2..... Fr,) can be represented as a finite composition of univariate continuous functions. Inspired by this, Liu et al. (2025) proposed the KAN, which re- places the linear weights in conventional MIPs with learnable univariate function units. This de- sign enhances the model's nonlinear modeling capacity with fewer parameters and improves inter- pretability. Building on this idca, recent studies have introduced KANs into various vision tasks and achieved promising results (Chen et al. [025bb Zhang et al. 2025a; Wang et al. 2025a Zhu et al. 2026). Among them, Li et al. (2025a) integrated Tok-KAN blocks into the U-Net framework to strengthen local feature modeling, providing higher accuracy, efficiency and interpretability for vision tasks. Despite the demonstrated success of KANs in unimodal scenarios, its potential for cross-modal alignment and fusion has yet to be systematically explored.
3 METHOD
Overview. Our proposed C2Seg comprises two sequential stages, as illustrated in Fig. Given in- put / and T. we first design CaCL. in the pretraining stage, where pairwise cosine similarity between text embeddings is computed to generate soft labels Yú; that guide robust image-text contrastive learning. In the fusion stage, we introduce BCAM, consisting of two paths that simultaneously generate vision-dominant features F,, and language-dominant features F,, enabling comprehensive exploitation of language information while preserving spatial structural information. Subsequently, K-Gate is applied to perform ditentioo-weiihhte of the two feature streams, and producing the fi- nal output Font- Finally, through skip connections and upsampling operations, the spatial resolution is progressively restored, yielding high-quality pixel-level segmentation predictions. Notably, we
3

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Stage i: Cluster-awore Contrestive Learning...
Stoge il: Bidirectional Complementory Fusion.
Frozened Text Encodey
Encoddy
UCAh
K-Gote
Prefivcion
My
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e)
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KAN-bosed Attention Goting (li-Gote)
Shape [8.0]
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Figure 2: Overview of the proposed C2Seg. Given medical images and their corresponding textual descriptions, visual and language features are extracted using dedicated encoders.
fine-tune the visual encoder with small learning rate while keeping the language encoder frozen to preserve stable semantic anchors in the text space.
3.1 FEATURE ENCODER
Based on the work of Li et al. (2025a) and Radford et al. (2021), we built a dual-branch encoder. The visual branch employs three convolutional layers followed by two KAN layers, aiming to combine the feature capturing ability of CNN with the nonlinear modeling capabilities of KANs. Given image 1 RCXHxW the visual branch produces multi-scale features V {Vo) with Ve RCxxHxxW, and progressively downsampled spatial sizes (H₃, We). A global image tor is computed as v = W, GAP((V) E R where GAP(()) denotes global average pooling and W. is learnable projection. On the language branch, the frozen CLIP encoder processes text T tokenized to length N, yielding position-aware token embeddings L E NR and sentence embedding Rd. To enable channel-aligned bidirectional interaction each visual scale, kens are projected by learnable per-scale 1x1 mappings as LE = LW, with We E RdxxCee hence Le NxC, The set (V:) preserves pixel-level spatial structure for subsequent fusion and de- coding, while {Le} together with I provide token-level and sentence-level semantics that feed into BCAM for bidirectional complementary interaction and fusion.
3.2 CLUSTER- CONTRASTIVE LEARNING
Unlike standard InfoNCE with single positive/negative pairing, CaCL reframes in-batch con- trastive learning as batch-wise semantic distribution matching. We first estimate text-text similarities in the frozen language space and convert them soft labels, which are then used supervise the image-text similarity distribution. This neighborhood wise probabilistic supervision systematically suppresses erroneous repulsive gradients induced by templated clinical phrasing. Throughout, image embeddings {v.) and sentence embeddings (1, == are L2-normalized.
Soft Label Construction. Given a batch of size B, we first compute the text-text cosine sim- ilarity matrix Mi cos(,, 1,). To attenuate the global similarity inflation induced by shared templates, we apply row-mean debiasing and non-negativity clipping to each row, obtaining M/, = max (M, μ4 0}. where 14; = 1
в Σn Mi-- Here, ,4 can be viewed as batch-level "template bias" whose removal helps suppress global template effects and yields a soft label distribution that focuses more on local semantic neighborhoods. Temperature is then used to produce semantic soft targets:
cxp(Mi,/T)
Y15 = ΣS exp((Mie
(I)
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To retain the anchor identity, the final target distribution mixes the diagonal with these soft targets as Yiy p Y,,+ (1 p) 1 = where p E [0, 1] balances the self- self-positive and its semantic neighbors.
Distribatiion-spprriiee Symmetric InfoNCE. Let the cross-modal logit be 8ij v. J. and define the directional probabilities Py-+ = softmax,(8s/1) and P! softmax, /T. We
match the predicted distributions to Y in both directions with a single bidirectional objective:
B
B
LCaCL = B
Yis log Pij + Yii log P.
Theoretical Justification. to any logit Siz is
With the above definitions of pu-t and pi-t, the gradient with respect
aLC.CT. 1 (PH Yi + p Yj²). Osy 7
Semantically related but non-matching pairs thus receive nonzero target mass (Yzz > and/or Yj > 0), which attenuates or even reverses the repulsive gradient, mitigating false negatives and promoting woighborhhod-consisee alignment. The additional computational cost is dominated by building M in O(BC) time, which is negligible relative the backbone.
3.3 BIDIRECTIONAL COMPLEMENTARY ATTENTION MODULE
Existing DualA-style bidirectional fusion methods typically update only the text tokens in their "language-dominatt" branch, without producing features that explicitly preserve image spatial in- formation. The representative work M3Att (Liu et al. 2023). although achieving bidirectional in- teraction between vision and language via multimodal cross-attention, uses single fully connected projection to compress the spatial dimension P into the channel dimension C' during fusion. This amounts to a non-structured mixing of all image patches for each text token, which weakens the original spatial inductive bias and tends to lose local details such boundaries and textures. There- fore, we propose BCAM, which constructs two complementary attention paths in parallel during the fusion stage, one vision-dominant and one language-dominant, dominant, that the two modalities dition each other and produce spatially aligned multimodal features directly on the pixel grid. This alleviates modality imbalance and preserves more local information. The structures and differences of the three attention mechanisms are shown in Fig. 3
PEC
KAN
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Pili Artenox
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Edirectiona Complementory Atsention Modiale (SCAM)
Figure 3:
The architecture of: (a). the generic attention mechanism; (b). the Multi-Modal Mutual Attention (M3Att;; (c). our proposed Bidirectional Complementary Attention Module (BCAM).
Complementary Attention. Given visual features V PXC (P H. x W denotes the number of spatial positions) and language features E R XC we use learnable KAN layers to obtain keys and values for cross-attention, defined as VLey KAN(V), V value KAN(V), Leey KAN(L), and Lyatue = (L). We then construct the scaled dot-product attention scores A:
A
Vkey key (LeKy) E PPN
(4)
5

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Vision-dominant Path. We apply softmax along the language axis N and use it to aggregate
language values, yielding a language- visual representation:
F.
Softmax, (A) Lvalue E
PxC.
(5)
The resulting F,, is sentence- and token-injected visual feature map that preserves the original spa- tial resolution P. Intuitively, each pixel position collects token semantics according to its relevance weights, providing rich, pixel-level semantic context.
Languate Path. Directly applying A E RNN to Vvalue would produce a token-level representation of shape RNx which lacks explicit pixel topology and thus hampers subsequent decoding. Therefore, we normalize the transpose A along the spatial axis P. apply each token's s spatial weights to the visual values, and then aggregate across tokens to obtain a language-guided feature aligned with the image grid:
N Σ 121
F
= N
Softmax p(A
[n,:])
O V E RPPC
(6)
where @ denotes broadcasted element- weement-wise multiplication over the channel dimension. The resulting F, encodes each token's 's collective attention over all spatial locations, forming a spatially coherent, language-guided feature map. Compared with a unidirectional, vision-only fusion scheme, the addi- tion of this language-initiated path mitigates modality imbalance, improves cross-modal alignment, and provides spatially consistent signals to the decoder, thereby enhancing downstream segmenta- tion performance.
3.4 KAN-BASED ATTENTION GATING
After the BCAM, the visual and textual streams have exchanged information bidirectionally: how- ever, modality statistical biases and noise patterns (e.g., imaging artifacts and templated phrasing) may still propagate across modalities and be amplified, thereby diluting spatial detail or inducing semantic drift. To address this, introduce KAN-based nonlinear gating mechanism that performs selective suppression and enhancement within each modality before fusion, followed by data-dependent mixing. This design improves the expressiveness of features and alleviates modal imbalance from the source.
For the two feature streams output by BCAM, the visual branch F. PxC and the language branch F, E Pxc we construct two independent KAN gating heads, each comprising two KAN layers with an intermediate ReLU and producing gating tensor that matches the input shape. We adopt tanh activation normalize each branch output, bounding the learned gating weights to [-1,1] 1]. Accordingly, the gating tensors are defined as:
g. = tanh (KAN (ReLU(KAN!) (F.))))
gi = tKAN (ReLU(KAN)))) (F.)) (7)
We then perform element-wise reweighting F element-wise multiplication. After this modality- refinement, we concatenate [F F along the channel dimension and apply 1 x 1 convolution for channel alignment and linear mixing to obtain the final fused representation Four- This process enhances the spatial perception ability of the model through feature selection, while providing a nonlinear and fine- grained selective path for cross modal fusion.
F gr and F O gi, where O denotes
4 EXPERIMENTS
4.1 DATASETS AND IMPLEMENTATION DETAILS
Datasets. We conduct experiments on four public medical image segmentation datasets: QaTa-
COV19 (Degerli et al. 2022), MosMedData+ (Morozov al. 2020 Hofmanninger et al.) 2020

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Table 1: Quantitative comparison QaTa-COV19 and MosMedDData+ datasets.
Method
Pa
Text
Dieette31
mloU115y
Qata-COV19
HD3S
ASSD)
Disett
mioUi/5y1
MnsMedlbata+
HEMS5]
ASSD]
NiNett Ronneberger
2015
31.4
79.02
69.46
33.95
9.00
551.73
23.52
6.35
U-Ner
74.5
70.25
24,06
19.1
70.81
22.75
75.07
25.31
Trams
UNet Clerrztal 2000
105.0
K
78.63
69.13
29,88
8.42
71:24
58.44
23:41
6.38
UKAN (Liera: p12N7
9.4
X
79.30
69.85
31.89
8.79
72.56
59.03
29,
7.25
MM-UK MM-UKWo-+ Yenain 11
2025a
9.9
79.30
69. 70
35.26
9.76
71.83
58.37
32.63
8.96
CTTP
(Macleed (1221)
87.0
19.1
11.25
5:34
71307
10.64
16.24
6:58
GLoR (1atineeee
45.5
79.94
70.68
26.47
5:21
72:42
60.18
28.61
6.79
TGANer
19.8
ConViKT (eaiin
35.2
70.58
LAVT Fil 2024
118.6
80.48
67.01
15.70
4.87
68.51
53.32
17.28
4.18
SLMT (11 K023
131.5
79.25
68.57
15.18
4.35
72.57
60.78
21.23
6.10
LViE Li 2r 20223
29.7
81.52
68.63
18.62
5.32
72.10
57.35
18.94
4.82
Unil aa 2074
28.7
72.88
59.58
15.15
4.11
65.80
52.01
19.98
4.96
RefSegformer
72.64
31.88
C2Seg (Ours)
1892
85.23
76.97
12.71
333
77.11
65.17
15,02
3.76
CVC-ClinicDB (Bernal al. 2015), and Kvasir (Jha el. 2020). Qa QaTa-COV19 and MosMedData+ are lung datasets with paired textual annotations provided by Li et al. (2024), while CVC-ClinicDB and Kvasir are colon polyp datasets whose image-text pairs are constructed following Zhang et al. (022).. We adopt Dice, mean Intersection over Union (mloU), the 95th percentile Hausdorff Dis- tance (HD95), and the Average Symmetric Surface Distance (ASSD)
primary evaluation metrics. Detailed descriptions of these datasets and the evaluation metrics are given in Appendix Sec. A.4 1.45o and Appendix Sec. A.5 respectively.
Implementation Details. Our proposed method is implemented on a server with two NVIDIA GeForce RTX 5090 (32 GB) GPUs. By default, the batch size is to 256 for the pretraining stage and 32 for the segmentation stage, the temperature parameter 7 is set to 0.07, and the soft- weight parameter p set 0.8. During the supervised segmentation stage, we adopt a combined BCE + Dice objective (BCEDice loss) as the training loss; a detailed formulation of this loss is provided in Appendix Sec, A.6 The initial learning rate set
10-3 for QaTa-COVil and CVC-ClinicDB and 3 x 10 for MosMedData+ and Kvasir. We use the Adam optimizer together with a cosine annealing learning rate scheduler.
Table 2: Quantitative comparison on CVC-ClinicDD and MosMedData+ datasets.
Methed
Params/M)
Text
Tioet
mlol1(55)
CVC-ClnieDB
HD95
ASSD
Thoar
mlol)(5)
Kvasir
HD9S
ASSDS
Konseherser
31.4
42.33
415241
65 (5
401.25
12.11
U-Net-
20.87
79.19
Swin
Upx 1oce 2024
82.3
X
81.19
71.54
26.38
8.67
77.24
66.90
21.25
5.30
UKAN (Lirra m02at
0.4
X
$9.74
84:47
13.19
3.72
87.77
81.13
21.37
5.82
MM-1
MM-UKWWeFF Zasiin
21 21
2025a
9.9
X
89.52
82. 15
13.26
3.36
85.63
78.03
24.81
7.12
LAVI Yang A10
118.6
B5.11
B2.76
0.35
1.35
91311
14.00
15.31
411
TGAN2T
19,8
89.93
B1.56
8.41
90.11
$4.19
17.18
4.33
RefSeytirmer Wire
195.0
78.77
MMIUNei [Bulzei 2004)
56.2
89.96
84
11.55
4.02
90.27
84.29
15.13
4.35
ReeLMIS (ir FuX63
23.7
81.31
73.04
38.44
12.13
90.63
84.35
17.93
4.43
MedLangVi VEr (wan or 202266
27.7
88.35
81.92
10.66
4.50
90.57
$4.21
14.12
4.09
ARSeg [Wang or 70:20
30.1
89.74
82.64
13.71
4.29
88.45
$1.34
22.79
566
C2Scg
1892
86.81
85.27
398
4.2 PERFORMANCE COMPARISON
We conduct systematic comparison between C2Seg and other segmentation methods on four pub- lic medical image datasets, with quantitative results summarized Tab. [ and Tab. 2 Overall, C2Seg achieves competitive or even state-of-the-art performance across all four benchmarks. Ben- efiting from the incorporation of textual modality, C2Seg significantly outperforms the strongest CNN-based model nne- and the KAN-based model UKAN. Compared with other multimodal approaches, our method not only excels on region-vverlap metrics (Dice and mloU), but also shows clear advantages distance-based metrics HD95 and ASSD, indicating that C2Seg provides more

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accurate delineation of lesion locations and boundaries and, from geometric perspective, further corroborating the benefit leveraging textual constraints for spatial localization.
As illustrated in Fig. text explicitly specifies "unilateral pulmonary infection, one infected area". yet several methods still misclassify regions in the right lung as lesions, whereas C2Seg correctly restricts its prediction to a single lesion the left lung. In the second row, the textual prompt "middle lower left lung" is given; some methods fail properly capture positional information and produce over-segmentation in the upper left lung, while C2Seg more accurately focuses on the middle-lower region of the left lung, demonstrating finer text-guided localization. In the fifth row, the text includes numerical cue "two infected areas"; some methods produce an incorrect number of lesions, whereas C2Seg matches both the spatial locations and the number of lesions described in the text. These qualitative observations are consistent with the quantitative gains and further confirm the advantage of C2Seg in adhering to textual constraints and improving mask consistency.
further analyze the behavior of different methods. In the first row, the
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Image
GT
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UKAN
LAVT
LVT
MedLangVir
C25eg
Figure 4: Visualization of different methods. Green, red, and blue represent true positive, false negative, and false positive pixels, respectively.
4.3 ABLATION STUDIES
Effeet of Proposed Components. We conduct systematic ablation studies on the MosMedData+ and CVC-ClinieDB datasets, summarized in Tab. 3 Case (a) keeps only the visual branch unimodal baseline; Case (b)-(f) all use CLIP as the text encoder, where (b) adopts DualA in Stage II, (c) replaces DualA with BCAM, (d) further adds K-Gate top of BCAM, (e) introduces ventional hard-label contrastive learning (HardCL) in Stage I, and the final full model corresponds Case (f). The results show clear step-wise improvement as components are added, validating both the effectiveness and complementarity of the proposed modules.
Effect of Text Encoders. Keeping the training framework fixed, further replace the text en- coder with domain -spccfcc biomedical language models BioBERT-Base Base v1.1 (Lee et al.] 2020) and PubMedBERT (Gu al. 2021). As shown in Tab. 3 even though CLIP text encoder is not pretrained on medical corpora, its performance remains slightly better than BioBERT and PubMed- BERT. We speculate this mainly because the CL.P is learned via contrastive training large-scale
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Table 3: Ablation study MosMedData+ and CVC-ClinicDB datasets.
Case
Text encoder
Stage
Stage II BCAM
MosMedDataa
CVC-ClmieeD
HardCL
CaCL
DualA
K-Gate
Diec(()]
mloU[%]]
Dice(
mloU(%))
73.61
60.02
86.59
78.11
(b)
75.59
62.35
89.56
83.14
90.31
CLIP
(d)
77.03
64.45
90.68
85,98
(c)
77.32
64.51
91.26
86.33
77.81
65.17
91.82
86.81
(g) BioBERT Base
76.22
64,18
90.60
86.08
64.76
image-text pairs, yielding an embedding space that is naturally aligned with visual features and bet- ter matched to the short, location- and quantity-oriented descriptions used in this work, whereas BioBERTT/PuMMedBRR are pretrained solely on pure-text biomedical corpora and are better suited for long clinical reports, lacking such cross-modat alignment and adaptation.
Effeet of Bidirectional Fusion Mechanism. To verify the effectiveness of the proposed bidirec- tional complementary attention, we fix the backbone and decoder and only replace the Stage II fusion module, comparing DualA with the proposed BCAM. The visualization results on QaTa-COV19 19 and Kvasir datasets are shown in Fig. Even when the prompt explicitly contains quantitative and po- sitional cues such "Unilateral," "two," and "lower left," DualA still tends to produce results that deviate from the text, for example activating both lungs or merging multiple lesions into a single region. In contrast, with BCAM, the high-response regions are much better concentrated on the text-specified target areas. This indicates that the language-dominant path effectively strengthens the mapping from textual semanties to spatial representations and, when combined with the vision- dominant path, not only alleviates mask inconsistency but also leads to more accurate lesion localization and boundary delineation.
Unferwol GEn FORVENE AAN Nyt
Weltten heo
choAk SEGOS)
polp
Shames pannwwy way
New 1oS sanques
meter modien
Medical Test
Image
GT
DuelA
BCAM
Medical Text
Image
GT
DuelA
BCAM
Figure 5: Qualitative comparison of DualA and BCAM on QaTa-COV19 and Kvasir datasets.
Effeet of KAN Structure. To assess the role of KAN in our framework, we design five struc- tural variants the MosMedData+ dataset: using pure CNN the visual encoder; adopting hybrid encoder with the first three layers as CNN and the last two layers as KAN; replacing the visual encoder entirely with pure KAN; replacing the KAN layers BCAM with linear projec- tions while keeping the encoder unchanged; and replacing the KAN layers in K-Gate with standard MLPs. As shown in Tab, a). the hybrid encoder yields consistent performance gains the pure CNN encoder, whereas the pure KAN encoder even leads to degradation, indicating that the local receptive fields provided by CNNs remain crucial for low-level feature extraction and that KAN is more suitable high-level complement rather than a full replacement. In addition, substituting KAN with linear layers or MI.Ps in BCAM or K-Gate results performance drops, suggesting that the nonlinear modeling capacity of KAN still offers advantages for higher-order cross-modal interactions.
Effect of Hyperparameters. We conduct hyperparameter sensitivity analysis the MosMed- Data+ dataset, with the results summarized Tab. 4b). For each experiment, all settings are fixed the default configuration (p=0.8, 700.07, batch size=256) except for the hyperparameter under

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Table 4: Ablation of KAN and hyperparameters MosMedData+ datasets.
Ga) Ablations on KAN.
(b) Sensalivily of bo perparameees
Settings
Dicetfo]1
mlo
Effect
Effect of
Effect of butch
(o
256)
(p
Hybrid mncuder
77.81
65.17
mloU(S3)
Dice
mleU(W0]
butch Naw
Dicat
mleti(S1)
KAN encoder
76.21
63.27
0.5
77744
65.05
0.055
27.39
6512
128
76.52
65.3
BCAM (Linear)
77.
64.42
0.8
77.81
65.17
0.07
77.81
68.17
256
77.81
65.17
K-Gate (MLP)
76.93
64.02
77.12
64.26
77.45
64.98
512
77.89
65.18
investigation. The results show that the model only weakly sensitive to p and т, and we therefore adopt the slightly better and more stable combination p=0.8 and r=0.07 as the default setting. In addition, as the batch size increases, the evaluation metrics exhibit a general upward trend, confirm- ing the positive effect of larger batches contrastive learning; however, the gain of 512 over 256 is marginal, so we choose batch size=256 in the main experiments trade-off between performance and computational cost.
Effeet of Contrastive Learning Strategy. We compare CaCL with two control settings (without contrastive learning and with generic contrastive learning) on the QaTa-COV19 and MosMedData+ datasets. For each dataset, we select the top 5 textual descriptions with the largest number of matched images and their corresponding image samples, and visualize the distributions of their image features in the latent space under different training strategies. As shown in Fig. o CaCL yields more compact and semantically better separated cross cross-modal features, confirming its effectiveness improving cross cross-modal alignment.
GoTo dotevet (Top-5 descristions)
MosMedDato- (Top-5 descriptions).
Figure 6: Visualization of image feature distribution on QaTa-COV19 MosMedData+ datasets under three pretraining strategies (no contrast, general contrast, and CaCI.). Each color represents all images corresponding to a textual description.
5 CONCLUSION
In this paper, we propose C2Seg, a two-stage medical image segmentation framework targeting text- mask consistency. In the pretraining stage, we first reformulate batch contrastive learning as cluster- level distribution matching, transforming text-to-text similarity into soft target to suppress false negatives and stabilize cross-modal alignment. In the fusion stage, we establish modality fusion path dominated by vision and language to more fully exploit textual information, while K-Gate provides fine-grained cross-modal information selection. Extensive experiments on four challenging datasets validate the superiority and effectiveness of C2Seg.
LIMITATIONS
In this work, we primarily rely on qualitative visualization to assess text-mask consistency and have not yet introduced a dedicated quantitative metric for this purpose. Moreover, the textual descrip- have not systematically examined the
tions in our datasets relatively clean and structured, model's 's robustness to complex text noise, such as spelling errors, abbreviations, or missing findings. Both aspects important directions that plan to explore in future work.
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ACKNOWLEDGMENTS
This work was supported in part by the Hefei Municipal Natural Science Foundation (2022009) and the Jiangsu Provincial Association of Traditional Chinese Medicine Project (CYTF2024089), Also, authors gratefully acknowledge the High-performance Computing Platform of Anhui University for providing computing resources.
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Xu Zhang, Bo Ni, Yang Yang. and Lefei Zhang. Madapter: A better interaction between image and language for medical image segmentation. In Marius George Linguraru, Qi Dou, Aasa Fer- agen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, and Julia A. Schnabel (eds.), Medical Image Computing and Computer Assisted Intervention MICCAI 2024, pp. 425-434, Cham, 2024. Springer Nature Switzerland.
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, and Curtis P. Langlotz. Con- trastive learning of medical visual representations from paired images and text. In Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, and Serena Yeung (eds.), Pro- ceedings the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA, volume 182 of Proceedings of Machine Learning Research, pp. 2-25. PMLR, 2022. URL URL https::/pprcceeeengg... mll wwwpppesssvvv..... vress//v82// w1t///11222// wwhag//g///aaaa..aaaaaaaa html
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: nested u-net architecture for medical image segmentation. Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer F. Mahmooo, Anne L. Martel, Lena Maier-Hein João Manuel R. Tavares, Andrew P. Bradley, João Paulo Papa, Vasilcios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, and Anant Madabhushi (eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 4th International Workshop. DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings, volume 11045 of Lecture Notes Computer Science, pp. 3-11. Springer, 2018. doi: 10. 0000000000000-5110000 URL https: /ttp///o...c.oo org 10. 1007 -- 0000--00300--0-------0-0--------
Junan Zhu, Zhizhe Tang, Zheng Liang, Ping Ma, and Chuanjian Wang. Kanseg: An efficient medical image segmentation model based on kolmogorov-arroll networks for multi-organ seg- mentation. Digital Signal Processing, 165:10 2026. ISSN 1051-2004. doi: https://doi.
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A APPENDIX
A.1 ETHICS STATEMENT
We have ensured that all data used in this research comply with ethical guidelines and proper cita- tions. The study follows responsible Al practices and avoids any data manipulation or misrepresen- tation.
A.2 REPRODICIBILITY STATRMENT
The experiments in this work are fully reproducible. All datasets and code used for model train- ing and evaluation are publicly available. Detailed information on the experimental setup, model configurations, and hyperparameters is provided to enable independent reproduction of results.
A.3
USE OF LLMS
This work does not involve the use of any large language models (LLMs), such as GPT, DeepSeek, or similar models.
A.4 DATASETS
To comprehensively evaluate the effectiveness of our proposed C2Seg, we conduct experiments on four widely used public medical image segmentation datasets: QaTa-COV19 MosMedData+, CVC- ClinicDB, and Kvasir. QaTa-COV 19 and MosMedData+ with textual descriptions curated by Li et al. (2024), which provide descriptive information for each image. A notable challenge in these two datasets is the high degree of text reuse: for example, 7,000 samples in QaTa-COV19 share only about 300 different textual descriptions, which poses additional difficulties for fine-grained vision-language alignment. In addition, we consider two colonoscopy polyp segmentation datasets, CVC-ClinieDB and Kvasir, where image-text pairs are constructed by /Zhang et al. (024) through clinically oriented descriptions of each image.
chest imaging datasets, both augmented
The QaTa-COV19 dataset Degerli et al. (2022), jointly released by Qatar University and Tampere University, comprises 9,258 chest X-ray images with pixel-level annotations for COVID-19 lesion segmentation. We follow the dataset partition protocol used in LViT et al. (202),, splitting the dataset into training set (5,716 images), validation set (1,429 images), and test set (2,113 images).
The MosMedData+ dataset Morozov et al. (2020); Hofmanninger et al. (2020) includes 2.729 chest CT slices labeled with infection masks for pulmonary lesions. Each image is also accompanied by textual descriptions that specify attributes such as infection laterality, anatomical location, and affected lung lobes, providing diverse spatial and semantic cues. We adopt the same data split in LViT Li et al. (2024): 2,183 training images, 273 validation images, and 273 test images.
The CVC-ClinieDB dataset Bernal et al. (2015) contains 612 colonoscopy images with pixel-level polyp masks collected from 29 colonoscopy video sequences. In this work, we use the text- augmented version released by ZZhang al. (2024), where each image is associated with concise clinical-style description. Following Zhang et al. (2024), we split CVC-ClinieDB into 550 training images and 62 test images.
The Kvasir dataset Jha et al. (2020) consists of 1,000 colonoscopy images with expert-annotated polyp masks acquired under diverse clinical conditions. Similarly, we adopt the text annotations provided by /Zhang et al. (2024) and follow its partition protocol, using 900 images for training and 100 images for testing.
A.5 EVALUATION METRICS
To quantitatively assess segmentation performance, adopt four widely used evaluation metries: Dice score, mean Intersection over Union (mloU), the 95th percentile Hausdorff distance (HD95), and the Average Symmetric Surface Distance (ASSD). Dice and mloU evaluate the region-wise
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overlap between the predicted segmentation and ground-truth annotations across all categories and spatial positions.
The Dice score measures the harmonic mean of precision and recall, placing more emphasis correctly segmented regions, and is defined as:
N
1
2|p Oygl
Dice
(8)
NC
|pyl + byy
where Pij and Bij denote the predicted and ground-truth pixel sets, respectively, for sample i and class
The mean Intersection over Union (mloU) reflects the average segmentation accuracy by computing the ratio of intersection over union for each class and averaging over the dataset:
N
1
Pij 0 yiy
mloU
(9)
NC
Paj U
where denotes the number of samples and C the total number of semantic categories. The notation Piy O Mijl and [pij U Dayl indicate the number of overlapping and combined pixels, respectively, between prediction and ground truth for each sample-category pair.
In addition to region-overlap metrics, also report two boundary- distance metrics, HD95 and ASSD, to assess the accuracy of lesion localization and contour delineation. Let Sprea and Sgt denote the sets of surface (boundary) points of the predicted and ground-truth segmentations, respectively, and d(x, S) = minyes || yl be the shortest distance from a point x to a surface S. We first compute all directed surface distances {d(a., Sg:)) x E Spred) } and {d(y, and take their union. HD95 is then defined as the 95th percentile of this pooled distance distribution, ie., the value below which 95% of all surface distances lie. A lower HD95 indicates that the worst-aligned boundary regions are relatively close to the ground truth.
Sgt)
The Average Symmetric Surface Distance (ASSD) measures the mean bidirectional surface distance between prediction and ground truth:
ASSD(Spred Sgt) 2
Σ xESpred
d(x, Set)
dly, Sprea) VESa
Σ
(10)
Sproat
|Szal
Smaller ASSD values indicate that, average, the predicted and ground-truth boundaries are closely aligned space. Together with Dice and mloU, HD95 and ASSD provide a more com- prehensive evaluation of both region overlap and boundary geometry.
A.6 TRAINING OBJECTIVE IN STAGE II
In Stage II, the segmentation decoder is optimized with hybrid Binary Cross-Entropy (BCE) and Dice loss, which combines stable pixel-wise supervision with region-overlap awareness. Let pe [0, 1] denote the predicted foreground probability for pixel i and y E {0,1) the corresponding ground-truth label (foreground or background), over all pixels indexed by = I,...,152 [S2] in the image domain S2.
The BCE loss is defined as
12
LBCB =
Σ
Iys log(p:) + yi) log(1 - -p:)],
(11)
Ω
which treats segmentation as a pixel-wiss wixe binary classification task and provides numerically stable gradients, especially in the early training phase.
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To explicitly encourage region-level overlap between the prediction and ground truth and alleviate class imbalance, we further adopt a soft Dice loss:
2
! i=
Peys
Dice = 1
(12)
10 21=1
!2
+
1== +
where e a small constant to avoid division by zero. This term focuses on the agreement between predicted and ground-truth foreground regions and is particularly beneficial when the lesion occupies only a small portion the image.
The final segmentation loss in Stage II is weighted combination of the two terms:
Leeg 0.5 LBCE 0.5 LDice-
(13)
In practice, this simple 1:1 weighting leverages the complementary strengths of BCE and Dice: the former stabilizes optimization at the pixel level, while the latter improves the overlap quality of the predicted masks, which is crucial for accurate medical image segmentation.
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Reviews:
[{"review_id": "oSEjS8jWP5", "reviewer": "Reviewer_WFDv", "summary": "This paper proposed a text-mask consistency enhanced two stage segmentation framework (with CaCL, BCAM, and K-Gate). The experiments have been done on two COVID datasets and showed clear improvement.", "questions": "please address the comments in weakness session. clarify the technical novelty, add more datasets, and maybe more evaluation metrics.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The methodology is sound and the paper is well-structured.\nThe paper’s motivation is clearly articulated by identifying specific causes of text–mask inconsistency, which justifies the two-stage approach. \nThe results show clear improvements in Dice and mIoU, and the ablation studies are thorough, which lends credence to the methodological claims.", "weaknesses": "1) The proposed C2Seg framework (with CaCL, BCAM, and K-Gate) appears to be a thoughtful integration.  While each component has roots in existing techniques (e.g. contrastive learning and dual attention are established concepts, and KANs have been explored in vision backbones), which require more detailed clarification on the technical contribution. \n2) The experiments use two public medical imaging datasets (QaTa-COV19 and MosMedData+), both are in the COVID-19 chest imaging domain, so the representativeness is somewhat narrow.\n3) One minor critique is that the evaluation emphasizes standard segmentation metrics without a direct quantitative measure of “text–mask consistency”, but qualitatively the improvements imply better alignment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TAtXSKdh3E", "reviewer": "Reviewer_bmue", "summary": "This paper tackles text-mask inconsistency in medical image segmentation through C2Seg, featuring Cluster-aware Contrastive Learning (CaCL) and Bidirectional Complementary Attention Module (BCAM). The motivation is clear and results show improvements, but I have concerns about novelty and experimental scope.", "questions": "Can you provide empirical evidence for CaCL's effectiveness by measuring actual false negative rates, gradient magnitudes, or negative pair distances before/after, and show sensitivity analysis for frozen encoder choice across domain-specific alternatives? The current analysis is purely conceptual without quantitative validation.\n\nWhy does Table 3 show pure KAN encoder underperforming hybrid design, and what's the rationale for hyperparameters (ρ=0.8, τ=0.07, batch size 256) without sensitivity analysis? How does text length variation affect the unnormalized summation in Equation 6?\n\nWhere are attention visualizations verifying BCAM's spatial preservation, failure case analyses for multi-site/multi-lesion scenarios, and computational cost comparisons (training time, memory, FLOPs) for practical deployment? How does performance degrade with noisy or incomplete text descriptions common in real clinical settings?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The problem is well-motivated—templated clinical text causing false negatives in contrastive learning is genuine, nicely illustrated in Figure 1.\n\n- Soft-label construction using text-text similarity is intuitive with clear mathematical formulation in Equations 1-3.\n\n- BCAM's spatial structure preservation addresses M3Att's limitation where fully connected projection loses local details.", "weaknesses": "The \"frozen strong baseline\" for computing text-text similarity lacks justification—medical text has domain-specific semantics that general CLIP might not capture well [1], yet there's no sensitivity analysis to different encoders like BioBERT or PubMedBERT. The row-mean debiasing M'ij = max{Mij - μi, 0} also needs theoretical grounding since hard thresholding might remove valid semantic structure [2], and the gradient analysis in Equation 3 is purely conceptual without quantitative validation of how much false negative attenuation actually occurs. The language-dominant path in Equation 6 sums over N tokens without normalization which should cause unstable magnitudes across varying text lengths, and BCAM's spatial preservation claim lacks attention visualizations to verify spatially-coherent patterns compared to M3Att.\n\nNovelty is limited since soft-label contrastive learning already exists in Prototypical Contrastive Learning [3], ProtoNCE [4], and medical-specific MedKLIP [5]. BCAM is essentially incremental modification of M3Att with changed spatial handling, and KAN integration is straightforward MLP substitution without domain-specific adaptation—Table 3 even shows pure KAN encoder performs worse than hybrid, contradicting claims about superior nonlinear modeling. The overall framework combines existing techniques without fundamentally new mechanisms for text-mask consistency.\n\nEvaluation is severely limited to two COVID chest imaging datasets, preventing generalization claims to diverse anatomies like brain MRI, abdominal CT, or pathology [6]. Missing comparisons with recent medical VLMs like LLaVA-Med, BiomedCLIP, SAM-Med, and MedSAM that directly target medical segmentation. No analysis of text quality robustness when reports have typos, abbreviations, or incomplete descriptions [7], and no computational cost reporting despite CaCL adding O(B²C) complexity and BCAM doubling attention paths—training time, memory, inference speed are all absent. Hyperparameter choices (ρ=0.8, τ=0.07, batch size 256) appear arbitrary without sensitivity analysis, and the \"text reuse\" problem (7000 samples sharing 300 descriptions) is mentioned but never correlated with performance gains.\n\n\n### Referenced Works\n\n[1] Müller et al., \"Joint Learning of Localized Representations from Medical Images and Reports\", Medical Image Analysis, 2022\n\n[2] Khosla et al., \"Supervised Contrastive Learning\", NeurIPS, 2020\n\n[3] Li et al., \"Prototypical Contrastive Learning of Unsupervised Representations\", CVPR, 2021\n\n[4] Dwibedi et al., \"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations\", ICCV, 2021\n\n[5] Wu et al., \"Medical Knowledge-enhanced Large Language Model\", Nature Medicine, 2024\n\n[6] Ma et al., \"Segment Anything in Medical Images\", MICCAI, 2024\n\n[7] Bozkurt et al., \"Reporting Standards for Clinical Decision Support\", Nature Digital Medicine, 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YSMiYrJOXk", "reviewer": "Reviewer_RDjv", "summary": "This paper proposes C2Seg, a two-stage vision–language framework for medical image segmentation, aiming to improve alignment between textual descriptions and segmentation masks. The first stage introduces Cluster-aware Contrastive Learning (CaCL), which converts intra-batch text similarities into soft labels for contrastive supervision. The second stage adds a Bidirectional Complementary Attention Module (BCAM) that integrates vision- and language-dominant paths, followed by a KAN-based Attention Gating (K-Gate) for nonlinear feature selection.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written, logically structured, and easy to follow.\n2. The topic of improving text–mask alignment in multimodal segmentation is relevant to medical AI.\n3. The authors conduct detailed ablation studies and provide reproducibility information.", "weaknesses": "1. The methodological novelty is limited, and the contribution appears primarily engineering-oriented. CaCL functions as a soft-label variant of conventional contrastive learning, closely aligned with supervised or semantic-aware contrastive paradigms. BCAM represents only a modest architectural modification to existing bidirectional cross-attention designs, while K-Gate simply replaces standard MLPs with KAN units without clear theoretical motivation or demonstrated substantive benefit. Overall, the paper assembles several established components—soft contrastive learning, dual-path attention, and nonlinear gating—without offering a cohesive conceptual advance or deeper theoretical insight.\n2. The central problem of “text–mask consistency” is not clearly defined. The paper lacks a formal metric or quantitative measure beyond Dice and mIoU, making it unclear whether the improvement truly reflects better semantic alignment or merely better segmentation accuracy.\n3. The experimental scope is narrow, relying solely on two COVID-related datasets with templated or synthetic text, and both tasks are limited to binary lesion segmentation. This setup is not representative of real clinical scenarios and does not evaluate the method under more challenging multi-class or multi-organ settings. As a result, the proposed approach remains highly restricted in scope, and the experiments do not demonstrate generalization to other organs, modalities, or real free-text clinical reports.\n4. The reported performance improvements are modest (≈1–2% Dice) compared with the additional complexity introduced by multi-stage training and multiple attention and gating modules. The computational cost–benefit trade-off is not justified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DHQYLDsQRp", "reviewer": "Reviewer_RBwK", "summary": "This paper proposes C2Seg, a vision-language framework for medical image segmentation that aims to achieve better alignment between clinical text descriptions and visual features. It introduces a Cluster-aware Contrastive Learning (CaCL) method that uses soft labels derived from text–text similarity in a frozen language space to address issues of repetitive and templated clinical phrasing, which can create false negatives in traditional contrastive setups. To fuse the two modalities, the paper presents a Bidirectional Complementary Attention Module (BCAM), which performs dual cross-attention between image and text tokens while preserving spatial structure. Finally, a KAN-based nonlinear gating mechanism (K-Gate) is added to selectively suppress or enhance modality-specific noise before fusion. Experiments on multiple medical segmentation datasets show moderate improvements in Dice and mIoU scores, suggesting that the proposed modules enhance cross-modal alignment and segmentation quality.", "questions": "Please address the points raised in the Limitations section and provide clarifications or responses to the concerns I posed there.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The main strength of the paper lies in its idea of using soft labels for contrastive learning through the proposed Cluster-aware Contrastive Learning (CaCL) module. Instead of relying on traditional hard negatives, which can misclassify semantically similar clinical descriptions as distinct, the method introduces a more flexible alignment strategy by estimating text–text similarity in a frozen language space and converting it into soft supervision. This is a smart and well-motivated way to handle the repetitive and templated nature of clinical text, and it effectively reduces false negatives during image–text alignment. The paper is also well-presented, with clear and visually appealing illustrations that help convey the framework and intuition behind each component. In addition, the authors conduct extensive quantitative experiments and compare their method against a wide range of state-of-the-art models, showing consistent, if moderate, performance improvements, which supports the effectiveness of their overall approach.", "weaknesses": "Although Figure 1 looks visually good, I think it is a bit overcomplicated for an idea figure. It includes too many technical details, which makes it harder to quickly understand the main concept. A high-level illustration focusing only on the problem and the core idea would be clearer.\n\nThe main motivation sentence, “clinical descriptions are highly templated and semantically repetitive”, feels vague. I understand the general point, but it does not clearly explain why this is an actual problem for learning. If the issue is about false negatives in contrastive learning due to repetitive phrasing, it should be stated more explicitly.\n\nFigure 2 is quite dense, and the overall pipeline is not very easy to follow. Some modules, like BCAM, appear in the diagram without a proper explanation. A short caption or visual cue describing what BCAM actually does and how it connects to other parts would help a lot.\n\nThe bidirectional fusion idea does not feel very new. Similar techniques have already been explored in earlier works such as [1] and [2]. In fact, the spatial relationship preserving approach here, where visual features are flattened into patch tokens, fused with text through cross-attention, and then reshaped back to the 2D grid, is essentially the same process described in [2]. The authors should clarify what is actually new in BCAM beyond this, for example, whether the “complementary role” assignment or the gating introduces any measurable improvement. A more detailed comparison with these existing techniques would help solidify the claimed novelty and make the contribution clearer.\n\nNever clearly explain what defines a cluster or how those soft labels are derived or updated during training. It seems the text–text similarity matrix is computed once from a frozen encoder, which makes the “cluster-aware” term a bit misleading; here is no actual dynamic clustering or adaptive grouping during training.\n\nThe motivation for using KAN is not convincing. The paper does not explain why a spline-based gating function is needed or what specific limitation of existing nonlinearities like ReLU, GELU, or MLP-based gates it solves. As it stands, KAN feels like it was just added as-is without a strong reason. The improvement shown in the ablation study is also quite small, so it is hard to tell if the gain comes from KAN itself or simply from adding another nonlinear component. A clearer justification and a fair comparison with simpler gates would make this part more credible.\n\nSince the entire claim revolves around better text–mask alignment, the paper should ideally visualize how text tokens influence spatial regions.\n\nNo formalized loss functions are described, nor is it explained how different objectives are balanced during training.\n\n[1] Cho, Yubin, Hyunwoo Yu, and Suk-Ju Kang. \"Cross-aware early fusion with stage-divided vision and language transformer encoders for referring image segmentation.\" IEEE Transactions on Multimedia 26 (2023): 5823-5833.\n\n[2] Sultan, Rafi Ibn, et al. \"BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentation.\" arXiv preprint arXiv:2503.23534 (2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)