GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
Authors: Mengzhu Wang
houcheng su
Jiao Li
Chuan Li
Nan Yin
Li Shen
Jingcai Guo
Venue: ICML 2025 poster (2025)
Keywords: Semi-supervised learning;medical image segmentation;GraphCL;clustered features
Primary area: deep_learning->selfsupervised_learning
TL;DR: GraphCL

Abstract: Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image
Segmentation
Mengzhu Wang' Houcheng Su
Jiao Li
Chuan Li*
Nan Yin
Li Shen
Jingcai Guo 6
Abstract
Semi- Semi-supervised learning (SSL) has made no- table advancements medical image segmen- tation (MIS), particularly in scenarios with lim- ited labeled data and significantly enhancing data utilization efficiency. Previous methods primar- ily focus complex training strategies to uti- lize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based based clus- tering for semi-supervised medical image seg- mentation (GraphC1.) by jointly modeling graph data structure in a unified deep model. The pro- posed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure infor- mation for semi-supervised medical image seg- mentation (SSMIS). Secondly, to get the clus- tered features across different graphs, we inte- grate both pairwise affinities between local image features and raw features as inputs. Extensive ex- perimental results three standard benchmarks show that the proposed GraphCL algorithm out- performs sfate-of-the-us semi-supervised med- ical image segmentation methods. The source code is available at https: : /ggtttb.....oo com/ dream ly/ GraphCI
GNN Chustering
Chattred Graph
Figure I. We apply graph neural networks (GNN) to address SS- MIS challenges. Specifically, create graph representation to capture the data structure, and then use GNN-based clustering to group the graphs
However, obtaining large medical datasets with precise la- bels for training segmentation models is challenging, as substantial amount of labeled images can only be provided by experts. This significantly limits the development of medical image segmentation algorithms and poses substan- tial challenges for further research due to the scarcity of labeled data. To address these challenges, semi-supervised medical image segmentation (SSMIS) (Bai et al., 2023; Wu et al., 2022; Shi et al., 2021) has emerged as an effective approach, enabling segmentation models to learn from a small set of labeled examples in conjunction with easily accessible unlabeled data.
1. Introduction
In the field of SSMIS, although labeled and unlabeled data are theoretically expected come from the same distribu- tion, in practice, due to the extremely limited availability of labeled data, it is challenging to accurately infer the true distribution of the data. This often leads to the issue of dis- tribution mismatch between the large amount of unlabeled data and the small set labeled data (Wang et al., 2019). To address this challenge, several SSMIS techniques have been developed. For example, BCP (Bai al., 2023) enhances data consistency by randomly cropping regions in labeled images (as foreground) and pasting them onto unlabeled images (as background). and vice versa. TI-ST (Gham- sarian et al., 2023) proposes a semi-supervised learning strategy called inttrmmaaion-ivvrrian self self-training, which
Medical image segmentation derives from computed tomog- raphy (CT) (Buzug, 2011) or magnetic resonance imaging (MRI) (Glover, 201 1), plays a crucial role in various clinical applications (Tang et al., 202la;b; Wang et al., 2024b;c:a).
'Hebei University of Technology Hong Kong University of Science and Technology 'University of Electronic Science and Technology of China "National University of Defense Technology Sun Yat-Sen University 'The Hong Kong Polytechnic University. Correspondence to: Nan Yin <yinnan891 @ @ggmal..oom>, Jingcai Guo <je-jingcai guo @ polyu.edu.hk>.
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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GraphCI
improves domain adaptation by evaluating the reliability of pixel-level pseudo-labels and excluding unreliable predic- tions during the self-training process. ContrastMask (Wang et al., 2022) implements dense contrastive learning on both labeled and unlabeled data. Recent advancements in the field have shown promising results, especially in traditional graph theory applications such as object detection (Zhao et al., 2021; Song et al., 2023) and tracking (Li et al., 2020a; Hyun al., 2023). However, all of these methods overlook the role of graph information in enhancing semi-supervised medical image segmentation.
structure information in graph for SSMIS.
We design a graph clustering objective as a loss func- tion optimize the correlation clustering task in SS- MIS.
Extensive experiments on popular medical image seg- mentation benchmarks show that GraphCL achieves superior performance.
2. Method
2.1. Notations and Definitions
Recently, GraphNet (Pu et al., 2018) make an innovative attempt in the supervised semantic segmentation field by applying Graph Convolutional Networks (Kip) & Welling, 2016; Wang et al., 2025) to the task. The re- searchers transformed images into unweighted graph struc- tures by aggregating pixels from superpixel techniques into graph
In medical image segmentation (MIS), a 3D volume is rep- resented as X E MCXWXHxD, where C, W, H, and D correspond to the channel, width, height, and depth, respectively. The goal of semi-supervised segmentation is predict a pixel-wise label map Y E {0. [Cx HXD,, indicating the distribution of background and target classes, with k representing the number of classes. The training set S comprises labeled data A and a much larger unlabeled dataset B, such that S = St U S... where S = ((}), Y is the labeled subset, and S. = (X)) is the unlabeled subset.
et al., 2012). These graphs were then fed into a standard GCN equipped with a cross-entropy loss function to generate pscudo-labela A (Zhang et al., 2021) propose an innovative affinity-based convolutional neural network capable of converting images into weighted graph forms. While these graph neural -aasrr-bbsed meth- ods have shown outstanding performance in traditional im- age tasks, they have received limited attention in the med- ical image domain. Medical image segmentation presents unique challenges, such as complex biological structures and high sensitivity to pathological changes. Moreover, no research has explored SSMIS specifically from the perspec- tive of data structure.
During training, we generate mixed samples by selecting two labeled images (X,,X; x²)) and two unlabeled images (X% X A foreground region is randomly cropped from X and pasted onto X to produce the mixed image X while another crop from X is pasted onto X to form X These mixed samples allow the network to learn compre- hensive semantic information, leveraging both inward (X"") and outward (X""t) perspectives.
To address this issue, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. When modeling data structures in the deep learn- ing network, we create a dense instance graph reflecting the structural similarity of the samples based on CNN fea- tures. Each node in the graph corresponds to the CNN features of a sample, which are extracted using standard convolutional neural network. Then, we deploy a Graph Convolutional Network (GCN) on this instance graph, al- lowing the structural information to be propagated through learnable weighted edges the network design. To fur- ther improve segmentation accuracy, we introduce a k-less clustering strategy that eliminates the need specify the number of clusters k, enabling similar nodes to automati- cally form clusters (see Figure 1). This strategy significantly enhances the flexibility and adaptability of the model. The core contributions of this study are summarized as follows:
Our method is built upon a teacher-student framework (Bai et al., 2023), where both networks adopt an encoder-decoder architecture. In the encoder, we incorporate structure- aware graph network to explicitly capture structural relation- ships between the inward and outward images. This graph network models spatial and semantic correlations, improv- ing the model's ability to understand the inherent structures within medical images. Additionally, the extracted features dynamically optimize the graph structure, refining cluster assignments to better represent shared and distinct charac- teristics across labeled and unlabeled data. Finally, both X and x are passed through the student network to predict the segmentation masks Yin and Your, which are supervised by the teacher network's 's predictions on the unla- beled images and the ground truth labels from the labeled images. The pipeline of our proposed method is illustrated in Figure
We propose a graph- clustering for semi- supervised medical image segmentation by modeling data structure in a unified network. To the best of our knowledge, this the first work to model the data
2.2. Bidirectional Copy-Paste Framework
The Bidirectional Copy-Paste (BCP) framework integrates
a teacher network T(X; m. X Ot) and a student network
2

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GraphCL
Images
X
Structure scores
features
x
EMA
update
features
Structure scores
CNN
festures
X00
Clustered Graph
Labeled images
Figure 2. The proposed Graph-hased Clustering for Semi-supervised Medical Image Segmentation (GraphCT.) architecture consists of two core graph mechanisms: GCN alignment and clustering graph construction. In the GCN alignment phase, data structure analysis network generates structured scores containing structural information, while the CNN is responsible for feature extraction. These structured scores are combined with the exterxxccte features to construct dense instance graph the GCN. After merging the features from both CNN and GCN, the system inputs them to align data from the same category. Regarding the clustering graph construction, we create similarity matrix based on the similarity between local features, which then as the adjacency matrix for graph. Finally. we utilize this adjacency matrix and deep features as node features to complete the graph construction.
S(Xi", X O.)) (Tarvainen & Valpola, 2017) enhance semi-supervised medical image segmentation (SSMIS) through coordinated training strategy, where θ and θ, are parameters. Initially, the student network is pre-trained using only labeled data to build a supervised model and teacher network leverage the pre-trained model to generate pseudo-labels for unlabeled data during self-training. In the pre-training phase, adapt the following strategy:
where X and X are labeled images, and denotes clement-wise multiplication. The corresponding pseudo- labels and ground truth labels are then combined to super- vise the student network using the following supervisory signals:
yin = Y O M ÿ ② (1-M) M) m
(5)
Y = =
M)
(6)
Y T(X% ④2)
(1)
We use weight a to control the contribution of unlabeled image pixels to the loss function. The loss functions for X and X are computed respectively by
TH = T(X., θ2)
(2)
where Xı and X are the unlabeled images, and Yu ÿuu are the corresponding probability maps. The pseudo-labels initialized using thresholding or argmax operations, de- pending whether the task involves binary multi-class segmentation.
Cin = Layy (Q Yin) CM + aLey (Q" Yin O (1 (1-M)
(7)
C Lug (Q You O (1(1-)))+aCa (Q You!
GM
(8)
In the bidirectional supervision phase, mixed images X and ouu are constructed using a mask M E {0, CxCWWxXxD indicating whether a voxel originates from the foreground background. These images are gen- erated follows:
where L is the linear combination of dice loss and cross- entropy loss. Q and Qout are computed by:
in = S, (x Q = S,
(9)
At each iteration, we update the parameters θ in the stu- dent network by stochastic gradient descent with the loss function:
i X M+ X @ M)-
(3)
x =
x
x (M
(4)
Lal L. Lout
(10)
3

---

GraphCL
Afterwards, teacher network parameters 6 1++1) 1)-th iteration are updated:
,
at the (A: +
nodes. The GCN operates on the instance graph, with the goal of refining the feature representations by aggregating information from neighboring nodes, thereby capturing both local and global structural patterns within the mini-batch. The graph convolution is performed as:
o = xo + (1-
(11)
where À is the smoothing coefficient parameter.
z = D AD X
(14)
2.3. Structural Graph Model for Segmentation
where Z is the output feature matrix, W is the learnable weight matrix, and D is the degree matrix associated with the adjacency matrix A. This operation ensures that each node in the graph aggregates feature information from its neighbors, weighted by the structural similarities encoded in A. This propagation mechanism allows the network to ex- ploit contextual information across the batch, improving its ability to segment complex anatomical structures in medical images, where the relationships between different regions are critical for accurate segmentation.
In fact, existing studies (Zhou et al., 2023; Robert et al., 2023; Huang et al., 2023) focus on modeling the data structure information for semantic segmentation and have achieved remarkable success, which further emphasizes the critical role of data structure information. To effectively model data structures in semi-supervised medical image seg- mentation (SSMIS), we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCI.).
To address the challenge of effectively integrating both labeled and unlabeled medical images within the semi- supervised medical image segmentation (SSMIS) frame- work, we propose a Structural Graph Model (SGM). The model is based on the idea that the spatial and semantic structure of images can be efficiently abstracted into a graph, where nodes represent the features of image regions, and edges describe the relationships between these regions. This graph structure enables flexible information propagation, which is crucial for enhancing segmentation accuracy, par- ticularly when labeled data is limited.
2.3.2. GRAPH NEURAL NETWORK CLUSTERING
To further cluster the same graph nodes, for each 3D image volume in mini-batch, we first extract deep features, re- sulting in a feature tensor F E [B where B is the batch size. Each voxel in the 3D volume serves as a graph node, with the feature dimensions (W, H, D) representing the spatial extent of the nodes. To capture re- lationships across the volume, we construct a graph where each node represents a voxel and edges connect spatially adjacent nodes or nodes with high feature similarity.
2.3.1. STRUCTURE-AWART ALIGNMENT
Specifically, the matrix W for the graph is derived based on spatial and semantic affinity between patches, calculated as follows:
In our graph construction framework, each sample in mini- batch is treated as node, and the relationships between nodes are modeled using a Data Structure Analyzer (DSA). This component generates structure scores that quantify the similarity between different samples based on their internal spatial structure, as derived from the learned CNN features. Formally, the feature extraction process for each 3D medical image Xbath is expressed as:
W=F.Fl Max
(15)
where F is the feature matrix and controls clustering sen- sitivity. T is used to adapt the cluster selection process in correlation clustering. Since the number of clusters cannot be directly selected correlation clustering, this parameter allows us control the sensitivity of the process, where higher values of correspond more clusters. This graph construction preserves important volumetric features, en- abling the GNN to recognize spatial and semantic relation- ships within the medical data.
= CONCCNNNNOO butch)
(12)
where X represents the graph signal, encoding the features of individual samples. These features are crucial in defining the graph adjacency matrix A, computed as:
A= X
(13)
Let Ñ denote as the node feature matrix, which is derived by applying one or more layers of Graph Neural Network (GNN) convolution on a graph G defined by an adjacency matrix W. Here, use a single-layer Graph Convolutional Network (GCN) to perform the feature extraction. The adjacency matrix W is constructed from the patch-wise correlation matrix based on features obtained from encoder. These correlations capture the relational structure of the patches within the visual data, thereby enabling the GNN
where X,, E R are the structure scores output by the DSA network, with w denoting the batch size and h the dimension of the structure features. The intuition is that samples with similar structural characteristics should have stronger connections in the graph, which facilitates effective feature propagation during the segmentation task.
Once the graph is constructed, we employ a Graph Convolu- tional Network (GCN) to perform feature propagation across
4

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GraphCL
to leverage spatial dependencies. Formally, the GNN layer maps the input node features N into refined node feature matrix N by learning the underlying data structure:
Method
Scons used
Metrics
Labeled Unlabeled
Dicef
Jaccardf
9SHD,
ASD]
V-Net (Milletari 2016)
415%)
52.55
47.05
Vetet (Milleturi et al., 2016) 8(10%)
0
82.74
71.72
13.35
3.26
V-Net (Milleturi al. 2016) S0CAI))
91.47
84.36
5,48
UA-MT (Yu et al., 2019)
82.26
70.98
13.71
3.82
Ñ = (N,, W; @GNN)
(16)
SASSNet (1.i et 20200)
81.60
69.63
16.16
3.58
DTC (L.u 2021a))
2021a)
81.25
69. 33
14,90
where OGNN represents the trainable parameters of the GNN layer. Following the GNN, we utilize Multi-Layer Perceptron (MIP) with softmax activation applied to Ñ to produce the final output S, which is the cluster assignment matrix. Each row of S corresponds to a node and represents the probability distribution over clusters, essentially encod- ing the likelihood of each node belonging to a particular cluster:
URPC (Luo et 2021b)
415%) 76(95%)
82.48
71.35
14,65
3.65
MC-Net (Wu
2021)
83.59
72.36
SS-Net (Wu et 2022)
86.33
76. LS
9.97
2.31
(Bai et 2023)
87.07
GraphCL
88,80
80.000
7.16
2.10 1
UA-MF (Yi et al., 2019)
81.79
78. 39
8.68
2.12
SASSNet al., 2020b)
87.54
778.0 78.05
DTC (Luo et al., 2021a)
87.51
78.17
8.23
2.36
URPC (Loo 1b)
8(10%) 72(90%)
86.92
77.03
11.13
MC-Net (Wu et al., 2021)
87.62
78.
10.03
1.82
(Wu al.,
88.55
79.
7.49
BCP (Bai et al., 2023)
89.39
80.92
7.26
1.76
S MLP(N; OMPP)
(17)
GraphCL.
90.24
82.31 6.42. 1.71
where MLP are the MLP's trainable parameters. The optimization of the GNN model is driven by clustering objective, with a loss function proposed to enforce distinct clustering properties.
Table I. Comparisons with state-of-the-ard semi semi-supervised seg- mentation methods on LA dataset. Improvements compared with the second best results are emphasized in bold.
We employ correlation clustering loss in this work, which directly promotes intra-cluster coherence and inter-cluster separation. This loss is defined as:
(ASD). Given two object regions, Dice and Jaccard mainly compute the percentage of overlap between them, 95HD measures the closest point distance between them and ASD computes the average distance between their boundaries. We have highlighted the results in bold when our proposed method outperforms the original counterparts.
Lec Tr(wss")
(18)
where Wi is redefined according to the specific correlation clustering requirements. This loss encourages nodes with high similarity (as per W) to be assigned to the same cluster (positive affinities), while penalizing connections between dissimilar nodes (negative affinities). Consequently, this approach is advantageous for scenarios where clusters have distinct internal structures where cluster boundaries are less clearly defined.
3.2. Implementation Details
All experiments use default settings of a = 0.5, x = 0.01 and t = 2. with fixed random seeds. LA Dataset experi- ments run on an NVIDIA A800 GPU, while Pancreas-NIH and ACDC datasets use an NVIDIA 3090 GPU.
At each training iteration, we update the parameters O, in the student network by stochastic gradient descent with the loss funetion(based on (10)):
LA dataset. Following SS-Net (Wu et al., 2022), we apply rotation and flip augmentations. Training uses SGD with an initial learning rate of 0.01, decaying by 10% every 2.5K iterations. We adopt a 3D V-Net backbone, with patches cropped to 112 x 112 x 80 and the size of the zero-value re- gion mask M is 74 53. Batch size 8, split equally between labeled and unlabeled patches, with pre-training and self-training at 5K and 15K iterations, respectively.
Call L.h + Lou + Lcc
(19)
We use a weight i to control the contribution of graph clustering to the loss function. Afterwards, teacher network
parameters 0 (1++1 at the (k + 1)-th iteration are updated.
ACDC dataset. Consistent with SS-Net (Wu et al., 2022), we use a 2D U-Net backbone with patch sizes of 256 x 256 and the size of the zero-value region of mask M is 170x 170. The batch size is 24, with pre-training and self-training at 10K and 30K iterations.
3. Experiments
3.1. Datasets and Evaluation Metries
All experiments are performed on three public datasets with different imaging modalities and segmentation tasks: Automatic Cardiac Diagnosis Challenge dataset (ACDC) (Bernard et al., 2018), Atrial Segmentation Challenge dataset (LA) (Xiong et al., 2021) and Pancreas-NIH dataset (Roth et al., 2015). Four metrics are used for evaluation, including the Dice Score (%), Jaccard Score (%), 95% Haus- dorf Distance (95HD), and the average surface distance
Pancreas-NIH. Based on CoraNet (Shi et al., 2021), data augmented via rotation, rescaling, and flipping. A laur-layer 3D V-Net is trained with Adam (Kingma, 2014), using an initial learning rate of 0.001. Cropped patches are 96 x 96 x 96, with the size zero-value regions of mask M is 64 x 64 x 64. Batch size, pre-training trainininng and self-training epochs are 8, 60, and 200, respectively.
5

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GraphCL
Method
Scans used
Metres
set 10%, GraphCL outperforms the second-best approach ASD]
Laboled Unlibeled
Dice
Jascardi
WSEDS
U-Ner (Rvencberger et ol., 2015) 365%)
0
47.83
57.01
51,16
12.62
by average of 3.85% across all four evaluation metrics.
U-Net (Rouneherger et al., 2015) PC108))
79.41
68.11
9.35
2.70
With the labeled ratio of 5%, we maintain strong advan- tage, showing an average improvement of 6.64 % over the second-best results across these metrics. This suggests that when the number of labeled volume is particularly limited,
Net (Ronneherger et 2015) 70(AIl)
0
91.44
84,59
4.30
0.99
UA-MT 201.))
46.04
7.75
SASSNa (1i 21200))
57.77
46.14
20.05
6.06
DTC (1.00 2021a)
56.90
45,67
23.36
7.39
URPC (Laood 2021b)
3(5%)) 67(95%)
55.87
44.64
13.60
3.74
MC-Ner (Wo et 2021)
62.85
52.29
7.62
2.35
SS.Net et
65.83
55,38
2.28
the knowledge from labeled data can be more effectively transferred to the unlabeled data. This phenomenon likely explains the superior performance gains observed when the labeled ratio is set to 5%. This observation also holds true for the ACDC dataset.
BCP (Bai et 2023)
$6,83
77,64
8:71
2.47
GraphCL
$8.68 1185
3.12|A47
0,88 1.9
UA- al,,
$1.65
70.64
6.88
2,02
SASSNer at. 20200)
84.50
74.34
5.42
1.86
DTC (Luo al2. 2021a)
84,29
73.92
12.81
4.01
URPC (Lao 202
((109) 630009)
83.10
72.41
4.84
1.53
MC-Net et al. 2021)
86.44
77.04
5.50
1.84
SS Net (Wu al.. 2022)
86.78
77.67
6.07
1.40
ADCC dataset. We also adopt the identical experimen- tal setup used in SS-Net. The averaged performance re- sults are shown Table 2 on the ACDC dataset for four- class segmentation. Our approach consistently outperforms all state-of-the-art methods across all evaluation metrics. With the labeled ratio is set to 10%, GraphCI. outperforms the second- best approach by an average of 26.01 % across all four evaluation metrics. With the labeled ratio of 5%,
al., 2023)
88.84
80.61
1.38
GrapbCL
89.31
$1.33 A17 2.10 0.66. (M72
Table 2. Comparisons with state-of-the-ar semi-supervised seg- mentation methods on ACDC dataset. Improvements compared with the second best results are emphasized in bold.
Scans used
Metrics
Method
Labeled Unlabeled
Dieef
Jaceard?
9SHD]
ASD_
V-Ner Beer(Minn (Milletari et ol., 2016)
69.96
$5.55
14.27
1.64
DAN (Zang et 2017)
76.74
63.29
11.13
2.97
GraphCL outperforms an average improvement of 29.65%
ADVNET (Vu al., 2019)
75.31
61,73
11.72
3.88
over the second-best results across these metrics. Our ap- proach leverages graph-based representations within the encoder and incorporates graph neural network clustering loss Lcc, which significantly contributes to the performance gains. Specifically, the integration of graph structures en- ables the encoder to capture complex spatial relationships and contextual dependencies among voxels, facilitating a more holistic understanding of the input data. Lcc encour- ages similar voxels to be grouped together while enforcing separation between distinct regions, thereby enhancing intra- cluster coherence and inter-cluster separability. Specifically, as can be seen in Figure 6, GraphCL can segment the fine details of the target organ, especially the edge details that are easily misrecognized or missed.
UA-MT (Yu al., 2019)
77.26
63.82
11.90
3,06
(Li SASSNet
20206)
12(20%) 50180%)
77.66
64.08
10.93
3.05
DTC (1.00 ot al., 2021a)
78.27
64.75
8.36
2.25
CoraNet (Shi et al., 2021)
79.67
66.69
7.59
1.89
BCP (Bai al.. 2023)
81.12
68.81
8.11
2,34
GraphCL.
R3.1
[143]
291 6.87]
1.1
Table 3. Comparisons with state-of- the---rrtt semi-supervised seg-
mentation methods on Pancreas-Ne NIH dataset. Improvements com- pared with the second best results are emphasized in bold.
3.3. Comparison with State-of-the-Art
We evaluate our framework on the LA and ACDC datasets, comparing it with several state-of-the-art methods, includ- ing UA-MT (Yu et al., 2019), SASSNet (Li et al., 2020b), DTC (Luo et al., 2021a), URPC (Luo et al., 2021b), MC- Net (Wu et al., 2021), and SS-Net (Wu et al., 2022). Addi- tionally, for the LA dataset, we include comparisons with V-Net (Milletari et al., 2016), while for the ACDC dataset, we compare with U-Net (Ronneberger et al., 2015). Fol- lowing the protocol in SS-Net, we conduct semi-supervised experiments with different labeled data ratios (i.e., 5% and 10%). For PanCreas-NIH dataset, we evaluate with la- beled ratio of 20% (Luo et al., 2021a; Shi et al., 2021). We benchmark our method, denoted as GraphCL, against various state-of of-the-art models, including V-Net (Milletari et al., 2016), DAN (Zhang et al., 2017), ADVENT (Vu et al., 2019), UA-MT (Yu al., 2019), SASSNet (Li al., 2020b), DTC (Luo et al., 2021a), and CoraNet (Shi et al., 2021).
Pancreas-NIH Dataset. For the Pancreas- dataset, we benchmark GraphCL against DAN (Zhang et al., 2017). ADVENT (Vu et al., 2019), UA-MT (Yu et al., 2019), SASSNet (Li et al., 2020b). DTC (Luo et al., 2021a), and CoraNet (Shi et al., 2021), all trained in a semi-supervised setup using both labeled and unlabeled data. V-Net is used as the backbone for our model and baseline methods, while V-Net alone is trained in a fully supervised manner as a lower bound. Table 3 shows that our approach achieves sub- stantial improvements in Dice, Jaccard, and 95HD metrics, outperforming the second- method by 2.50%, 3.79%, and 0.72%, respectively.
3.4. Ablation Studies
LA dataset. To ensure a fair comparison, we adopt the identical experimental setup used in SS-Net. As shown in Table 1, our approach achieves superior performance across all four evaluation metrics, completely outperforming com- peting approaches. Specifically, when the labeled ratio is
To analyze the effectiveness of each component in our pro- posed framework GraphCL. we conduct series of ablation studies across three datasets (LA, ACDC, and Pancreas- NIH) with varying labeled data ratios. The detailed results
6

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GraphCL
LA
ACDC
Baseline SA Lcc
Scans used
Metrics
Scans used
Metrics
Labeled Unlabeled
Dicet
Jaccard
95HD2,
ASD,
Labeled Unlabeled
Dicef
Jaccard
95HDJ
ASD
x
87.07
77.42
8.83
2.15
86.83
77.64
8.71
2.47
88.13
78.93
7.46
1.93
88.13
79.46
5.26
1.45
4(5%) 76(95%)
3(5%)
67(95%)
88.34
79.24
8.68
2.27
87.80
78.93
2.58
0.93
v
88.80
80.00
7.16
2.10
88.68
80.32
3.12
0.88
89.39
80.92
7.26
1.76
88.84
80.61
4.42
1.38
90.00
81.88
6.87
1.74
89.52
81.61
3.27
0.97
8(10%)
72(90%)
7(10%)
63(90%)
x
88.79
80.05
8.24
2.19
89.53
81.68
2.98
0.89
90.24
82.31
6.42
1.71
89.31
81.33
2.10
0.66
Table Ablation study. The best results are emphasized in bold.
Pancrcas-NIH
GCN layers at deeper levels (specifically at the fifth layer)
Bascline SA Loc
Scans used
Metries
leads to the highest performance gains. For example, with labeled ratio of 10% on the ACDC dataset, inserting the GCN at the fifth layer results in Dice Score of 89.31 % and ASD of 0.66, which significantly better than inserting GCN at shallower layers. This demonstrates that deeper placement of GCNs allows the model to capture more complex spatial dependencies and contextual information, thereby enhancing segmentation accuracy.
Labeled Unlaheled
Dicef
Jaceardf 9SHD
81.12
68.81
8.11
2.34
v
x
82.47
70.53
7.15
2.42
12(20%) 50(80%)
x
82.32
70.34
10.56
3,63
83.15
71.42
6.87
2.12
Table Ablation study. The best results are emphasized bold.
Scans used
Metries
Layers
Labeled Unlabeled
Dicef Jaccard
95HD ASD]
Dataset-Specific Observations.
Each dataset demonstrates unique performance patterns based on the labeled data ra- tio and the presence of SA and Loe. For example, on the Pancreas-NII dataset (Table 5), combining SA and Lee results in improvements of 2.50% in Dice Score and 0.72 in 95HD over the baseline. The clustering loss Lcc con- tributes substantially to boundary delineation by enforcing inter-cluster separability, which particularly beneficial for segmenting complex anatomical structures.
1
0.030
0.016
80.51
45.53
2
72.59
61.30
17.12
5.22
3
3(5%)
67(95%)
87.83
79.04
3.24
0.98
4
88.05
79.35
4.17
1.19
5
88.68
80.32
3.12
0.88
1
0.008
0.004
40.94
23.73
2
72.35
59.20
15.14
4.32
3
7(10%)
63(90%)
88.72
80.40
4.01
1.15
4
89.10
80.99
5.94
1.43
These ablation studies highlight the importance of each component in GraphCL, demonstrating that the combination of structure-aware alignment and graph-based clustering significantly improves segmentation results across various medical image datasets.
5
89.31
81.33
2.10
0.66
Table Ablation study on ACDC. The best results emphasized in bold.
are presented Table 4, Table 5, and Table 6.
Effectiveness of Components. In these experiments, we ex- amine the contribution of two key components: the structure- aware alignment (denoted as SA) and the graph neural net- work clustering loss (Lcc). As shown in Table 4, adding SA or Ece individually improves performance compared to the baseline. Specifically, incorporating both SA and Lee achieves the best results, with notable improvements in met- rics such as Dice Score, Jaccard Index, 95HD, and ASD. For instance, on the LA dataset with 10% labeled data ratio, GraphCL with both components achieves Dice Score of 90.24%, outperforming the baseline by substantial margin.
Antaul
DEID
A50
0.01
(a)
(b)
Figure 3. Sensitivity analysis on the ACDC dataset with the labeled ratio of 3(5%).
Optimal Placement of GCN Layers. We further inves- tigate the impact of placing the Graph Convolutional Net- work (GCN) layers at different depths within the encoder, as shown Table The results indicate that inserting the
3.5. Impacts of prree-pparaeters
To further verify the effectiveness of the proposed method, we also conduct sensitivity analysis on the ACDC dataset

---

GraphCL
81
Diet
DV
59
82
85
isccind
10
S3RD
(a)
(h)
Figure 4. Sensitivity analysis on the ACDC dataset with the labeled ratio of 7(10%).
GT
SSNet
BCP
GraphCL
Figure 6. Visualizations of several semi-supervisee segmentation methods with 5% labeled data and ground truth ACDC dataset. The blue, red, and orange lines represent the 25%, 50%, and 75% locations the segmented area, respectively.
3.6. Visualization Analysis
Figure and Figure display presents kernel density es- timations and segmentation results for different methods trained on the ACDC dataset, trained with 5% labeled data. In Figure 5, among all three cardiac structures, GraphCL has the best alignment of feature distributions between la- beled and unlabeled data. This is evident when compared to SASSNet, SSNet, and BCP. Figure illustrates that the segmentation results from GraphCL are notably more accu- rate and precise. In contrast to SSNet and BCP, GraphCL. presents tighter and more distinct boundaries, demonstrating closer aligmment with the ground truth (GT). These find- ings underscore GraphCI''s superior capability in capturing critical features and enhancing segmentation performance, particularly in scenarios with limited labeled data.
0.2
0.3
02
SASSNet
SSNet
BCP
GraphCI.
Figure 5. Kernel dense estimations of different methods, trained on 5% labeled ACDC dataset. Top to bottom are kernel-dense esti- mations of features belonging three different classes of ACDC: left ventricle, myocardium, and right ventricle.
to evaluate the impact of hyperparameters and т, with labeled data ratios of 5% and 10% (Figure and Figure 4).
Effect of x (Figure 3 (a) and Figure 4 (a)). controls the weight the structure-aue aware alignment. Increasing k initially improves the Dice and Jaccard scores, reaching peak around 0.01, after which performance declines slightly. This indicates that moderate achieves the best balance between alignment and segmentation quality.
4. Conclusion
In this paper, we propose novel graph-based clustering for semi-supervised medical image segmentation (SSMIS) that models graph data structures within a unified framework. Our approach leverages CNN-derived features from samples to construct a densely connected instance graph, based on the structural similarity, which effectively captures semantic representations for SSMIS. Additionally, we introduce graph clustering mechanism to utilize more information during the clustering process, enabling implicit semantic part segmentation. Extensive experiments demonstrate the effectiveness of our proposed approach. For future work, we will explore methods to generate more reliable labels and enhance graph accuracy, aiming to reduce noise within the input graph.
Effect of (Figure 3 (b) and Figure 4 (b)). controls clustering sensitivity for the clustering loss Lec. т = 2 yields the most consistent overall performance across multi- ple metrics, with Dice, Jaccard, 95HD. and ASD showing stable, favorable results. Although there instances where certain metrics reach peak values at the variation in results between these values and = 2 is relatively small. Therefore, - = 2 can be considered an optimal choice, as it offers reliable performance without sig- nificant orade-offs, making a robust setting for clustering sensitivity.
and 10,
8

---

GraphCL
Impact Statement
Kipf, T. N. and Welling, M. Semi-supervised classification with graph convolutional networks. ICLR, 2016.
This paper presents work whose goal to advance the field of Semi-supervised Image Segmentation. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Li, J., Gao, X., and Jiang, Graph networks for multiple object tracking. In WACV, pp. 719-728, 2020a.
Li, S., Zhang, C., and He, X. Shape-aware semi-supervised
3d semantic segmentation for medical images, In MIC-
Acknowledge
CAL, pp. 552-561, 2020b.
This work is supported by the National Natural Science Foundation of China under Grants No. 62406100, Tian- jin Natural Science Foundation under Grants No. 24JC- QNJC00320, Beijing Postdoctoral Research Foundation. This work is also supported by funding from the Hong Kong RGC General Research Fund (No. 15221 15216424/24E, and 1521 15/25E,, the PolyU Internal Fund (No. P0056171), and the Huawei Gifted Fund.
Luo, X., Chen, J., Song, T., and Wang, G. Semi-supervised medical image segmentation through dual-task consis- tency. In AAAI, volume 35, pp. 8801-8809, 2021a.
Luo, X., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Chen, N., Wang. G., and Zhang, S. Efficient semi- supervised gross target volume of nasopharyngeal car- cinoma segmentation via uncertainty rectified pyramid consistency. In MICCAL, pp. 318-329, 2021b.
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10


Reviews:
[{"review_id": "rk8DBUjgP6", "reviewer": "Reviewer_itcY", "summary": "The paper proposes GraphCL, a novel graph-based clustering framework for semi-supervised medical image segmentation (SSMIS). The key contribution is integrating graph data structures into deep learning models which leverages both labeled and unlabeled data, leading to better segmentation performance. The authors propose a dense-connected instance graph constructed from CNN features, combined with a Graph Convolutional Network (GCN) to propagate structural information. Additionally, they introduce a k-less clustering strategy to automatically group similar nodes without specifying the number of clusters. The method is evaluated on three public medical image segmentation benchmarks (ACDC, LA, and Pancreas-NIH), demonstrating superior performance over state-of-the-art methods. Ablation studies confirm the effectiveness of structure-aware alignment and graph clustering.\n## update after rebuttal", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">Q1: Essential References Not Discussed\n\nA1: We acknowledge the relevance of works like GraphSAGE (neighborhood aggregation)[1], GAT (attention mechanisms)[2], Graph U-Nets (hierarchical pooling)[3] and MixMatch (unifiy dominant approaches)[4]. Different from this methods, GraphL uniquely address semi-supervised medical image segmentation by constructing dense instance graphs for structural similarity learning and k-less clustering, leveraging both labeled and unlabeled data. We will incorporate these comparisons in the revised manuscript to improve our work.\n\n>Q2: Lack insightful discussion of data structure information\n\nA2: Medical images exhibit strong geometric regularity in organ/lesion morphology (e.g., topological connectivity of cardiac chambers). Our graph-based approach leverages this by enforcing structural consistency constraints on segmentation boundaries when annotations are scarce and propagating anatomical knowledge from labeled to unlabeled regions through graph convolutional message passing. From the ablation study in Table 4, our method achieves significant performance improvement by incorporating graph structural information into the CNN framework, as it effectively preserves the tree-like branching patterns of capillaries at local scales while simultaneously maintaining the spatial constraints between vessels and organs at global scales.\n\n>Q3: Computational complexity\n\nA3: To empirically assess the computational cost of our approach, we conduct experiments on the BraTS2019 dataset using an NVIDIA RTX 3090 GPU. During the training phase, we set the batch size to 4, following previous studies, and observe a memory consumption of only 3697 MiB for GPU and 9 GiB for system memory. In the inference phase, the GPU memory consumption further reduces to 2745 MiB, while the system memory remains at 9 GiB. Additionally, our model exhibits a computational complexity of 119.512G FLOPs, which is considered moderate, and a parameter count of 15.747M, indicating a lightweight architecture. These results demonstrate that our method maintains computational feasibility even on large-scale datasets, addressing potential concerns regarding scalability. Furthermore, the total training time is 2 hours, further confirming the efficiency of our approach.\n\n\n>Q4:Discussing potential limitations in GCNs\n\nA4: Compared to traditional methods, GCN-based approaches require additional matrix multiplications and neighborhood aggregation steps, which can increase computational complexity, especially for large-scale datasets. To mitigate this issue, existing optimization techniques such as mini-batch training, efficient sparse matrix operations, and model pruning can be employed. Moreover, exploring lightweight graph neural network variants or hybrid approaches could further reduce computational costs while maintaining performance. \n\n>Q5:Visualize the graph structures and clustering results\n\nA5: To enhance the clarity and intuitiveness of our method, we have added t-SNE visualizations at https://anonymous.4open.science/r/tsne-E479/Visualization.pdf. \n\n>Q6: Clarify the impact of different dataset sizes on performance improvements\n\nA6: To further validate the effectiveness of our model, we conducted additional experiments on the BraTS 2019 dataset (Labeled 10% and Unlabeled 90%). It can be observed that our method achieves a significant improvement on the large-scale BraTS 2019 dataset, particularly with the Dice score increasing from 78.11% to 82.02%, demonstrating the superiority of our approach on large-scale datasets. \n\n| Method        | Dice↑  | Jaccard↑ | 95HD↓ | ASD↓ |\n|--------------|--------|----------|-------|------|\n| BCP          | 78.11  | 67.63    | 12.34 | 1.96 |\n| **GraphCL**  | **82.02** | **71.75** | **10.30** | **1.93** |\n\n>Q7: Performance on imbalanced class distribution\n\nA7: The ACDC dataset is a classic class-imbalanced dataset, where the pixel distribution differences between the myocardium and ventricular cavities provide a real-world scenario for studying imbalanced segmentation problems. As shown in Table 2, our method demonstrates a significant improvement, which validates its effectiveness on imbalanced data.\n\n>Q8: Considering the very limited number of labeled data\n\nA8: We consider the very limited number of 20% labeled data in Table 1 and and one labeled data (1/70) in Table 2, experimental results demonstrate that our method achieves significant performance improvement even with limited training data.\n\n| Method  (Labeled (20%))      | Dice↑  | Jaccard↑ | 95HD↓ | ASD↓ |\n|--------------|--------|----------|-------|------|\n| BCP          | 89.62  |81.77    | 3.03 | 0.99 |\n| **GraphCL**  | **90.40** | **83.02** | **12.60** | **0.72** |\n\n| Method  (Labeled (1/70))      | Dice↑  | Jaccard↑ | 95HD↓ | ASD↓ |\n|--------------|--------|----------|-------|------|\n| BCP          | 58.54  |45.92    | 50.35 | 21.70 |\n| **GraphCL**  | **68.81** | **57.46** | **33.96** | **13.80** |", "claims_and_evidence": "The paper claims that GraphCL is the first approach to model data structure information in graph form for SSMIS and that it achieves state-of-the-art performance. The empirical results support these claims with strong improvements in segmentation accuracy across multiple datasets. The authors provide extensive ablation studies to validate the effectiveness of each component (e.g., structure-aware alignment and graph clustering loss). The results show consistent improvements across most metrics, particularly in scenarios with limited labeled data.", "methods_and_evaluation_criteria": "Using graph-based clustering to capture structural relationships in medical images is innovative and addresses the challenge of limited labeled data. The evaluation is conducted on standard datasets with widely accepted metrics (DSC, Jaccard, 95HD, ASD). The choice of benchmarks (ACDC, LA, Pancreas-NIH) is appropriate, as they include diverse medical imaging tasks and modalities (CT, MRI).", "theoretical_claims": "The authors provide a clear formulation of the graph construction and clustering mechanisms. The paper does not present formal theoretical proofs.", "experimental_designs_or_analyses": "The experiments are well-designed, with thorough ablation studies and sensitivity analyses to validate the impact of key components (graph clustering and structure-aware alignment) and hyperparameters (e.g., κ and τ). The datasets used are appropriate for the task, and the results are consistently reported. The paper could benefit from a discussion of the computational complexity of the proposed method, especially in comparison to existing approaches.", "relation_to_broader_scientific_literature": "The paper builds on prior work in semi-supervised learning and graph-based methods for medical image segmentation. It extends the use of GCNs to SSMIS, which has not been extensively explored in the medical imaging domain. The authors effectively position their work within the broader literature, citing relevant studies in semi-supervised learning, graph neural networks, and medical image segmentation.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths:\n1.The integration of graph-based clustering with semi-supervised learning is novel and addresses a critical challenge in medical image segmentation.\n2.Strong empirical results with multiple datasets and baselines. The ablation studies and sensitivity analyses provide strong evidence for the effectiveness of each component of the proposed method.\n\nWeaknesses:\n1.There lacks insightful discussion of why data structure information helps fine-grained semi-supervised medical image segmentation. This limits the methodological contribution of the proposed method. The rebuttal partly solved this problem.\n2.The paper lacks a discussion of the computational complexity of the proposed method, which could be a concern for large-scale datasets that contain many unlabeled data. This problem has been solved in the rebuttal.", "other_comments_or_suggestions": "1.Consider discussing potential limitations such as computational overhead from GCN operations with existing methods, which would provide valuable insights for practical applications. This problem has been solved in the rebuttal.\n2.The paper would benefit from visualizations of the graph structures and clustering results to provide a more intuitive understanding of the method. This problem has been solved in the rebuttal.\n3.Clarify the impact of different dataset sizes on performance improvements. The current datasize (both labeled and unlabeled) is too small to genralize to large-scale datasets. This problem has been solved in the rebuttal.", "questions_for_authors": "No", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "No significant ethical concerns were identified."}}, {"review_id": "h5ViNPOcuD", "reviewer": "Reviewer_jPp7", "summary": "This paper introduces a graph-based clustering for semi-supervised medical image segmentation by modeling data structure in a unified network. A graph clustering loss function was proposed to optimize the correlation clustering task in SSMIS.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">Q1: About the novelty\n\nA1: For the importance of graph structural information, our method leverages two types of graph structural information: spatial relationships between voxels/pixels and semantic relationships based on feature similarity. Specifically, we construct dense instance graphs to capture structural information from CNN features, propagate this information through GCNs, and employ correlation clustering to group similar nodes. These mechanisms collectively enhance the model's expressive capability, allowing it to better utilize the structural information within images. Meanwhile, medical images inherently exhibit topological relationships in anatomical structures, such as organ connectivity and tissue continuity. Given the limited availability of labeled data, leveraging these structural priors is crucial for semi-supervised learning. \n\nFor the novelty of the graph-based perspective, unlike Sun et al [1], our method does not merely use graph cuts as a post-processing step. Instead, it integrates graph learning in an end-to-end manner, modeling both local and global structural relationships while leveraging graph clustering to refine feature representations, rather than focusing solely on boundary optimization. Compared to Li et al [2], we are the first to introduce correlation clustering for SSMIS and propose a k-less clustering strategy, which automatically determines the number of clusters, eliminating the reliance on hyper-parameter selection. Existing graph-based methods are primarily used for regularization (e.g., graph cuts) or consistency enforcement. In contrast, our work is the first to model data structure as a learnable component, utilizing graph clustering to discover latent semantic relationships. \n\n\n>Q2:  About SGM\n\nA2: The Structural Graph Model (SGM) mentioned in the paper serves as a fundamental framework for semi-supervised medical image segmentation (SSMIS), designed to effectively integrate both labeled and unlabeled medical image data. Although Figure 2 does not explicitly label the SGM module, it is embedded within the model—for instance, as a graph-structured processing layer following feature extraction or incorporated into the design of graph-based loss functions. The core working principle of SGM relies on constructing a graph structure to enable semi-supervised learning, where pixels or regions of an image are represented as nodes, and their similarities form the edges. Labeled data nodes act as \"anchor points,\" providing supervised information, while unlabeled nodes receive semantic information through graph convolution or message-passing mechanisms, thereby facilitating label propagation. This approach leverages the relational structure of the graph to propagate knowledge from limited labeled data to unlabeled samples.\n\n\n>Q3: About internal spatial structure\n\nA3: We recognize that our original description may have been unclear - the \"internal spatial structure\" we refer to does not originate directly from the CNN feature maps themselves, but rather emerges from the graph representation of sample relationships. Specifically, in our framework: (1) each node in the instance graph represents a feature vector extracted by a standard CNN from an individual sample; (2) the spatial relationships are then constructed at the graph level through learned connectivity patterns between these node features; and (3) the structure scores quantify similarity based on these graph topological relationships rather than direct spatial correlations in the CNN features. \n\n>Q4: About the Data Structure Analyzer\n\nA4: The Data Structure Analyzer (DSA) is primarily responsible for computing structure scores, which quantify the similarity between different samples and guide the GCN in graph construction. In Figure 2 , this component is positioned between the CNN feature extraction stage and the GCN processing stage. In Eq. (12), $\\mathbf{X}$ represents the features extracted by the CNN, serving as the graph signal. In Eq. (13), $\\mathbf{X}\\_{\\text{sa}}$ denotes the structure scores. Specifically, $\\mathbf{X}\\_{\\text{sa}}$ is computed from $\\mathbf{X}$ and is utilized to construct the adjacency matrix $\\hat{\\mathbf{A}}$, which models the relationships between samples. \n\n>Q5: About the larger dataset\n\nA5: To further validate the effectiveness of our model, we conducted experiments on the BraTS 2019 dataset. The BraTS 2019 dataset comprises multi-institutional pre-operative MRI scans from 335 patients. It can be observed that our method achieves a significant improvement on the large-scale BraTS 2019 dataset, particularly with the Dice score increasing from 78.11% to 82.02%, demonstrating the superiority of our approach on the large-scale datasets. \n\n| Method        | Dice↑  | Jaccard↑ | 95HD↓ | ASD↓ |\n|--------------|--------|----------|-------|------|\n| BCP          | 78.11  | 67.63    | 12.34 | 1.96 |\n| **GraphCL**  | **82.02** | **71.75** | **10.30** | **1.93** |", "claims_and_evidence": "The authors claim that 1) previous methods neglect the importance of graph structural information, and 2) no research has explored semi-supervised medical image segmentation (SSMIS) from the perspective of data structure. However, they do not specify what graph structural information can be utilized or explain why it is crucial. This claim is not well substantiated, as the incorporation of GCN into the framework yields only a modest performance gain, suggesting that graph structural information may not be as critical as the authors suggest. Regarding the second claim, there are existing works that have explored the use of graphs in semi-supervised medical image segmentation.\n\n[1] Sun, Junxiao, et al. \"Semi-supervised medical image semantic segmentation with multi-scale graph cut loss.\" 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021.\n[2] Li, Gang, et al. \"Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation.\" Neural Networks 184 (2025): 107063.", "methods_and_evaluation_criteria": "Incorporating a graph into the SSMIS framework is a viable approach. However, the lack of a high-level explanation and sufficient technical details raises questions about whether the proposed method will have a significant impact on the problem at hand.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experimental design seems acceptable overall. However, the significant variation in hyperparameters across datasets raises concerns about the method’s generalizability and its usability across different applications.", "relation_to_broader_scientific_literature": "The paper addresses an area of active research in semi-supervised medical image segmentation, which has been well-explored. Methods like MT, UA-MT, DTC and co-training-based methods, have already demonstrated effective results in this domain. However, the approach proposed in this paper is based on MT with GCN-based regularization, offering limited innovation in terms of methodology or application. The performance gains appear to be marginal, and as such, it is unclear whether the proposed method can substantially advance the field. A clearer demonstration of its advantages over existing approaches would help establish its novelty and relevance.", "essential_references_not_discussed": "Since the proposed method targets SSMIS, I recommend reviewing related papers in more detail. For instance, co-training is a significant approach in SSMIS, but relevant papers are not discussed.", "other_strengths_and_weaknesses": "1. In the abstract, it is stated that 'The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs.' However, these two points are not advantages. The statement after 'firstly' is more of a novelty claim, which may not be accurate, while the sentence after 'secondly' describes a feature of the proposed method rather than an advantage.\n\n2. Terms like Structural Graph Model and Data Structure Analyzer are not widely established or standardized, causing difficult to read.\n\n3. An overview of the proposed method is necessary to help readers gain a clearer understanding, but it is missing.", "other_comments_or_suggestions": "I have no other comments or suggestions.", "questions_for_authors": "1. Could you provide more specifics on the type of graph information that can improve semi-supervised medical image segmentation? How is this information effectively utilized in the proposed method?\n\n2. It is stated that \"To address the challenge of effectively integrating both labeled and unlabeled medical images within the semi-supervised medical image segmentation (SSMIS) framework, we propose a Structural Graph Model (SGM).\" I don't know what SGM is? It is not shown in Figure 2. And how does it integrate labeled and unlabeled medical images?\n\n3. It is stated that \"...This component generates structure scores that quantify the similarity between different samples based on their internal spatial structure, as derived from the learned CNN features.\" However, I am unsure why CNN features would contain internal spatial structure. Can you explain?\n\n4. What the so-called Data Structure Analyzer is like? What is the relation between the $X$ in equation 12 and $X_{sa}$ in equation 13?\n\n5. I noticed that the three datasets used in the paper are all small. Why would you choose small datasets to demonstrate the utility of SSMIS? Unlike large labeled datasets, small datasets are relatively easy to curate. It would be more impactful to test the proposed method in real-world scenarios with larger, more challenging datasets.", "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": "gNkPqQ0mvq", "reviewer": "Reviewer_L7XD", "summary": "In this work, the authors tackle semi-supervised medical image segmentation (SSMIS) by proposing GraphCL. This is the first work to model data in a graph network for SSMIS. The authors propose a graph clustering loss function for optimization.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review and constructive feedback on our manuscript. We are grateful for your positive remarks and are pleased that you found our work to be a valuable contribution to the field of semi-supervised medical image segmentation.\n\n>Q1: The standard deviation of the performance is missing, however. The authors would benefit by showing standard deviation and conducting t-test to determine if the performance improvement is statistically significant or not.\n\nA1: Thank you for your valuable feedback. We appreciate your suggestion regarding the inclusion of standard deviation and statistical significance analysis. Since we have four metrics: Dice, Jaccard, 95HD, and ASD, where Dice and Jaccard indicate that higher values are better, while 95HD and ASD indicate that lower values are better, it is not possible to compute the standard deviation as suggested by the reviewer because these four metrics represent different meanings. Consequently, since the standard deviation cannot be computed, the t-value also cannot be calculated. We sincerely appreciate the reviewer’s suggestion from a statistical perspective. Thank you for this constructive suggestion that has helped improve our work.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "- The authors introduce a k-less strategy for clustering (k = number of clusters), enabling similar nodes to automatically form clusters.\n- They follow a teacher-student framework, the outputs of which are used to construct the graph data structure. The graph loss-function is used to train the whole framework in an end-to-end fashion.\n- The different components of the method are well-motivated", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "- The authors compare with adequate baselines, which are state-of-the-art methods in SSMIS task. They compare across three publicly available medical image seg datasets. They conduct experiments under different unlabeled % data settings as well.\n- The authors use 4 metrics (Dice, Jaccard, HD, ASSD) to evaluate the segmentation quality. The authors' proposed GraphCL outperforms all the baselines. \n- The standard deviation of the performance is missing, however. The authors would benefit by showing standard deviation and conducting t-test to determine if the performance improvement is statistically significant or not. \n- The authors provide good ablation studies of the different components in their method.\n- Appreciate the code release in the supplementary.", "relation_to_broader_scientific_literature": "The current work has real-life applications as medical datasets tend to have few labeled and largely unlabeled data. As the authors' work outperforms existing SSMIS methods, it has relevance to the community.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "N/A", "questions_for_authors": "none.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The main technical novelty is a regularization loss based on correlation clustering for semi-supervised medical image segmentation. Graphs are constructed with each sample in a batch as a node or each voxel as node, and graph convolutional networks propagate features on a dense graph, for which similarity is based on dot product between node features.

Though there is a lot of work already leveraging clustering or graphs for semi-supervised segmentation, this paper is the first to propose graph clustering specifically k-less correlation clustering for this problem. Extensive experiments are given with noticeable improvement over previous methods across multiple datasets of various sizes.

This paper received divergent reviews with 2x accept and 1x reject. Two reviewers think the improvements are significant. Reviewer jPp7 who recommends to reject, was concerned about the novelty of this work and suggested a few related works that utilized graphs in the formulation. Reviewer jPp7 also requested experiments on a larger-scale dataset. The AC believes most of the concerns have been addressed with additional experiments and clarification.

However, the presentation of the paper has much room to improve. As reviewer jPp7 pointed out, it is very confusing to introduce terminologies that are not common, such as Data Structure Analyzer and Structural Graph Model. The authors should improve the presentation and also include clarification on structure information and insights in the final version.
Decision: Accept (poster)