MAGIC: Rethinking Dynamic Convolution Design for Medical Image Segmentation

Authors: shijie  li
zheng  li
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-02 16:44:43

Abstract: Recently, dynamic convolution shows performance boost for the CNN-related networks in medical image segmentation. The core idea is to replace static convolutional kernel with a linear combination of multiple convolutional kernels, conditioned on input-dependent attention function. However, the existing dynamic convolution design suffers from two limitations: i) The convolutional kernels are weighted by enforcing a single-dimensional attention function upon the input maps, overlooking the synergy in multi-dimensional information. This results in sub-optimal computations of convolution kernels. ii) The linear kernel aggregation is inefficient, restricting the model’s capacity to learn more intricate patterns. In this paper, we rethink the dynamic convolution design to address these limitations and propose multi-dimensional aggregation dynamic convolution (MAGIC). Specifically, our MAGIC introduce a dimensional-reciprocal fusion module to capture correlations among input maps across the spatial, channel, and global dimensions simultaneously for computing convolutional kernels. Furthermore, we design kernel recalculation module, which enhances the efficiency of aggregation through learning the interaction between kernels. As a drop-in replacement for regular convolution, our MAGIC can be flexibly integrated into prevalent pure CNN or hybrid CNN-Transformer backbones. The extensive experiments on four benchmarks demonstrate that our MAGIC outperforms regular convolution and existing dynamic convolution. Code is available at: https://github.com/Segment82/MAGIC

Main paper: 
 introduction: 
 Automatically segmenting various data modalities (such as CT and MRI scan) is one of the most fundamental yet challenging tasks in medical image analysis [25, 52] . On the one hand, accurate segmentation allows healthcare professionals to access reliable morphological data, and assists in making precise and dependable diagnoses [55] . On the other hand, medical images commonly contain the varying scale of organs or lesions, low contrast, and blurred edges [14, 22] . These distractors pose a formidable challenge to segment the organs with intricate organizational structures.Due to the scale invariance and inductive bias of convolution operation, CNNs are widely adopted to tackle the above challenge. Accordingly, many pure CNN architectures or hybrid CNN-Transformer architectures have been designed for medical image segmentation [6, 15, 20, 29, 33, 38, 51] . For instance, UNet [38] pioneered the use of fully CNN for medical image segmentation, leveraging convolutions to build hierarchical feature representations. In parallel, TransUNet [6] incorporates the strengths of both CNNs and Transformers, which employs the capability of CNNs to capture high-resolution, informative representations in the spatial domain. With these representations, Transformers can further capture their long-range dependencies for achieving better segmentation results.These CNN-related methods usually opt for the regular convolutions as their backbones. Nevertheless, since the inherent spatial invariance and channel specificity, regular convolutions struggle to adapt to various visual modalities across different spatial locations. Further, the principle of weight sharing impede their effectiveness in extracting features from varying scale targets with blurred edges. In addition, most of them typically introduce extra convolutional layers or enlarge the dimensions of convolutions, such as kernel size and channel number. Hence, these methods aiming at boosting CNNs capabilities significantly increase computational costs. In short, the above limitations hinder the models' flexibility and diminish their capacity to generalize effectively, especially when dealing with the intricacies of medical imagery.Recently, Lei et al. [28] attempt to introduce the dynamic convolution [9] for medical image segmentation, in order to mitigate the limitations of regular convolutions. The core idea of this method is to replace static convolutional kernel with a linear combination of multiple convolution kernels, according to the input-dependent Squeeze-and-Excitation (SE) [19] attention mechanism. In doing so, the weight coefficients is adaptive for the organs or lesions with intricate organizational structures. This can increase the representation capability without increasing the depth or width of the network. Furthermore, each convolutional kernel only needs to be computed once, thereby reducing extra computational cost in comparison to regular convolutions.Despite the encouraging progress, there are two major limitations for this method. (1) The convolutional kernels is weighted by enforcing the SE attention function upon the input features. This operation only processes information from a single channel dimension, which emphasizes the critical feature channels about lesion edges or organ textures, as illustrated in Figure 1 (a). However, other dimensional (spatial, global) information within the input is disregarded, where spatial structural information between pixels contain the unique textures and edges of organs/lesions; global information identifies key features across the entire input. (2) The linear combination of multiple convolutional kernels restricts the model's capacity to learn more intricate patterns, because it mainly relies on the additive property of kernels for combination, while overlooking the aggregation of multiple kernels. For the above limitations, we assume that (1) modeling the synergy among three dimensions helps the convolutional kernels understand "what and where the organ/lesion is", thereby enhancing the model's generalization ability; (2) modeling more diverse context information within input feature to compute the convolutional kernels, while increasing the parameter efficient of kernel aggregation.In this paper, we tackle the above limitations by proposing a novel Multi-dimensional AGgregation dynamIc Convolution (MAGIC), which learns dimensional-reciprocal convolutional kernels to capture varying scale of organs or lesions, while aggregating multiple kernels to maintain parameter efficiency. As a plug-andplay replacement, MAGIC can seamlessly substitute the regular convolution in pure CNN or hybrid CNN-Transformer backbones for medical image segmentation. Architecture-wise, given feature maps from the backbones, we first design a Dimensional-Reciprocal Fusion (DRF) to capture their correlations across the spatial, channel, and global dimensions in parallel, which can attain a comprehensive understanding for them to compute the multiple convolutional kernels. Then, unlike traditional linear combination, we design a Kernel Recalculation (KR), which learns the interaction among multiple kernels to generate scalar for each kernel, thus enhancing the overall parameter utilization. Further, the aggregated convolution kernels is used to distill the feature maps of complex organs or lesions in medical images. Finally, these feature maps are fed into the backbones to obtain the accurate segmentation results.Our contributions can be summarized as follows:• We make in-depth analysis for the limitations of regular convolution and vanilla dynamic convolution, when using them to extract the complex feature in the medical image (e.g., varying scale of organs or lesions).  
related work 2.1 medical image segmentation: 
 Convolutional neural networks (CNNs) have been the de-facto standard for the medical image segmentation [17, 34, 37, 40] . UNet [38] pioneered the application of CNN for medical image segmentation task, which introduced a U-shaped fully convolutional network.Inspired by the simplicity and high performance of UNet, numerous variants of U-shaped CNNs have emerged to enhance the segmentation accuracy of models [21, 23, 26, 57] . Benefiting from the robust feature extraction capabilities of convolution, CNNs play an indispensable role in medical image segmentation. Recently, motivated by the success of Transformer architectures [12, 13, 35, 44, 45] , some works have attempted to combine CNNs with Transformers [42, 49, 56] . For example, TransFuse [54] introduces a parallel strategy that explores the balance between combining CNNs and Transformers for maximizing the advantages offered by both. Given that CNN can compensate for the limitations of Transformer structures in attending to local information, the hybrid CNN-Transformer networks have powerful feature learning ability.Numerous prior studies have demonstrated the efficacy of dynamic convolution in deep neural networks [11, 24, 41] . Brabandere et al. [24] introduced dynamic filters in the convolution layer, conditioned on an input. Yang et al. [50] and Chen et al. [9] replaced the static convolutional kernels with 𝑛 convolutional kernels, which were weighted using an attention mechanism over the input. Based on this idea, WeightNet [36] employed grouped fully connected layer to generate the convolutional weight directly. However, this vanilla dynamic convolution resulted in an n-fold increase in the number of convolutional parameters. To mitigated this limitation, Li et al. [32] proposed a more compact model via matrix decomposition, which learnt a base kernel and a sparse residual to approximate dynamic convolution. ODConv [31] introduced a more generalized form of dynamic convolution, utilizing multidimensional attention to explore different dimensions of the convolutional kernel space for generating kernel weights. Recently, Lei et al. [28] introduced DDConv, a integration of deformable convolution and vanilla dynamic convolution, designed to adaptively change the weight coefficient and deformation offset for increasing the precision of medical image segmentation.The attention mechanism plays a pivotal role in various vision tasks [8, 19, 43, 46, 53] . For instance, Li et al. [30] utilize local information as a guide to spatially activate the feature representation. Hu et al. [19] introduce a novel architectural unit called the Squeezeand-Excitation (SE) block, which computes channel correlation to enhance important channel feature maps. To complement the channel attention block, Chen et al. [7] suggest a spatial attention block to refine focus on a feature map. Recently, Wang et al. [48] draw inspiration from traditional methods [3] and extend the classical non-local operation to deep neural networks. 
method: 
 In this section, we first revisit the foundational concepts of vanilla dynamic convolution via a general formulation. Subsequent sections will elaborate on the formulations of our MAGIC.Definition: Vanilla dynamic convolution, such as DYConv [9] and CondConv [50] , replaces static convolutional kernel with a linear combination of 𝑛 convolutional kernels {𝑊 1 ,𝑊 2 ...𝑊 𝑛 } weighted by an attention mechanism, as follows:EQUATIONEQUATIONwhere {𝛼 1 , 𝛼 2 ...𝛼 𝑛 } denote attention scalars learned by the attention function 𝜋 (•); 𝑥 ∈ R 𝐻 ×𝑊 ×𝐶 and 𝑦 ∈ R 𝐻 ×𝑊 ×𝐶 are input and output features, where 𝐻 , 𝑊 , and 𝐶 denote the height, width, and number of channels respectively. For brevity, we omit the bias term in this paper. Limitation Discussions. As described in Equation 2, the dynamic property of vanilla dynamic convolution arises from the aggregation of multiple convolutional kernels, which are computed based on the exploration of the input space via an attention mechanism 𝜋 (•). Consequently, exploring the input space and aggregating kernels are two key components of dynamic convolution. However, both DYConv [9] and CondConv [50] employ a modified SE [19] structure as the function 𝜋 (•) to capture the channel-wise correlation of the input, thereby generating the attention scalars {𝛼 1 , 𝛼 2 ...𝛼 𝑛 }. In other words, these methods overlook the exploration of other dimensional contexts within the input features, such as spatial information and long-range dependencies, which are crucial for understanding the medical dissection of structure and tissue. Furthermore, capturing such relationships is inherently challenging for regular convolution. Such a rudimentary exploration of the input space might be one of the reasons why vanilla dynamic convolution only marginally outperforms traditional convolution in medical image segmentation tasks, as explored in Section 4. Additionally, each convolutional kernel is weighted by attention scalars {𝛼 1 , 𝛼 2 ...𝛼 𝑛 }, which are outcomes of the attention mechanism 𝜋 (•). This indicates that the weights of convolutional kernel are exclusively dependent on the input features, treating each kernel with identical importance. As a result, the importance across different kernels are not properly reflected.Building upon the above analysis, we introduce the Dimensional-Reciprocal Fusion (DRF) module to harness synergies from three distinct perspectives: spatial, channel-wise, and global dimensions, as illustrated in Figure 1 (b) .Firstly, we also employ the SE type approach to explore both spatial and channel-wise correlations. Unlike DYConv and Cond-Conv, which focus solely on squeezing the channel-wise dimension, our method compresses both channel and spatial dimensions concurrently. Specifically, for an input maps 𝑥, we apply average pooling operation along the spatial dimension to obtain the feature maps 𝛽 𝑐 ∈ R 𝐶 ×1×1 . Simultaneously, we utilize both average and max pooling operations on the input along the channel dimension. These pooled features are then concatenated, resulting in a feature maps 𝑥 ′ ∈ R 2×𝐻 ×𝑊 . Subsequently, we replicate these concatenated feature maps along the channel dimension 𝐶/2 times. Finally, we apply average pooling along the spatial dimensions, yielding the condensed feature maps 𝛽 𝑠 ∈ R 𝐶 ×1×1 . The process is formulated as:EQUATIONEQUATIONwhere 𝐶𝑎𝑡 [•, •] represents the concatenation operation along the channel dimension; Pooling 𝑐/𝑠 represents the pooling operations along channel-wise and spatial dimensions; F 𝑟 * denotes reshape operation.Then, to capture unaltered long-range dependencies within an input 𝑥, we incorporate a non-local operation [3, 48] alongside pooling operations. This operation computes the correlations of a position with all positions in the input map, thereby obtaining the global context. Mathematically, given a input maps 𝑥, the response 𝜔 𝑖 at position 𝑖 with all positions in the input maps is defined as:EQUATIONwhere 𝑁 𝑝 = 𝐻 × 𝑊 is the number of positions in the feature map 𝑥; 𝐿 𝑣 * is linear transformation matrices; F (𝑥 𝑖 , 𝑥 𝑗 ) denotes the relationship between position i and j; 𝐶 (𝑥) is a normalization factor. For practical applications, the Embedded Gaussian function, an advanced version of the Gaussian function F (𝑥 𝑖 , 𝑥 𝑗 ) = 𝑒 𝑥 𝑇 𝑖 𝑥 𝑗 , is utilized to calculate similarity in an embedding space, and 𝐶 (𝑥) is softmax function:EQUATIONwhere 𝐿 𝑘/𝑞 * is linear transformation matrices. Inspired by the discoveries in [4] that showed attention maps for different query positions to be nearly identical, we employ a simplified non-local operation which use a query-independent attention map for all query positions, to further minimize computational demands:EQUATIONwhere 𝛽 𝑔 ∈ R 𝐶 ×1×1 is a global attention map. The process as illustrated in Figure 2 . Finally, we combine the feature maps { 𝛽 𝑠 , 𝛽 𝑐 , 𝛽 𝑔 }, which encapsulate diverse types of information, to compute the attention scalars 𝛼:EQUATIONEQUATIONwhere 𝐹𝐶 (•) is fully connected layer. Essentially, these three types of information are synergistic, providing a robust performance foundation for discerning complex contextual cues. We will validate these advantages with experimental evidence in Section 4.Instead of the traditional linear combination, we introduce Kernel Recalculation (KR) for kernel aggregation to facilitate the interaction among kernels. Specifically, we employ an KR module 𝜙 (•) to calculate correlation scalars {𝑘 1 , 𝑘 2 . . . 𝑘 𝑛 } from feature vector 𝛽 in DRF for weighting convolutional kernels:EQUATION)where the KR 𝜙 (•) computes the importance of each convolutional kernel. Specifically, it first projects the feature vectors into a compact space, reducing computational costs and identifying essential relationships through a linear matrix mapping. Subsequently, it computes attentions over the number of convolutional kernels. Finally, it generates the weight of kernels by a sigmoid activation function, as shown in Figure 2 (b). The operation of the KR 𝜙 (•) can be formally represented as: where BN(•) represents batch normalization, and 𝐿 1/2 * are the learnable linear matrices. The core of aggregation is that the calculation of the convolutional kernel is no longer oriented towards a single input feature, and the interactions between the kernels also affect the calculation of the convolutional kernel. Moreover, the utilization of a non-linear combination facilitated by the attention function allows for a more nuanced and flexible adaptation to the complexities inherent in feature representation. Following [47] , we split the dataset into 70 (1304 axial slices), 10 (182 axial slices), and 20 cases for training, validation, and testing.EQUATIONGlaS Dataset. GLAnd segmentation (GlaS) datatset [39] comprises microscopic images of slides stained with Hematoxylin and Eosin (H&E). The dataset includes 165 images: 85 images designated for training purposes and 80 images allocated for testing.Skin Lesion Segmentation. We utilize the ISIC 2017 dataset [10] for skin lesion segmentation, consisting of 2000 dermoscopic images for training, 150 for validation, and 600 for testing. Following the setting in [1] , we resize all images to 192×256. 
implementation details: 
 Backbones. We employ UNet [38] and ResNet [18] as CNNs backbone to assess dynamic convolution. Following DYConv [9] and CondConv [50] , we apply dynamic convolution for all convolution layers except the first layer. Besides, we use TransUNet [6] and TransFuse [54] as hybrid CNN-Transformer backbones for a comprehensive evaluation.Experimental Setup. We utilize UNet, ResNet, and TransUNet architectures with dynamic convolutions on the Synapse and ACDC datasets. For the GlaS dataset, we apply both TransUNet and UNet [16] as the segmentation head. For the UNet and TransUNet frameworks, dynamic convolution is implemented in the CNN encoder. All models were trained for 150 epochs on the Synapse dataset. For the ACDC dataset, training was extended to 200 epochs. Following the Trans-Fuse, all models were trained for 30 epochs on the ISIC 2017 dataset.For the GlaS dataset, the models were trained for 100 epochs. For fair-comparison, we use publicly available codes and adhere to popular training and testing configurations used by the community. All models are trained under same settings without any pre-trained models.In the Synapse dataset, we employ mean Dice coefficient (mDICE), Hausdorff Distance at 95th percentile (HD95), mean Intersection over Union (mIoU), and Average Surface Distance (ASD) as evaluation metrics, reporting DICE scores for individual organs. We utilize mDICE and HD95 for evaluating the ACDC dataset. In the GlaS dataset, we incorporate mDICE, mIoU and HD95 as the metrics.For the ISIC 2017 dataset, we employ four metrics for assessment: mDice, Sensitivity (SE), Accuracy (ACC), and Specificity (SP). Comparative Results on Synapse Dataset. Table 1 presents a comparison of our MAGIC against existing dynamic convolution methods in three backbone architectures (UNet, ResNet 18, Tran-sUNet) on the Synapse dataset. We can observe that our MAGIC always outperforms other methods, achieving the highest performance gains on all backbones. In contrast, other dynamic convolutions, such as ODConv (4×), provides slight enhancements in the mDice score. Furthermore, backbone models with our MAGIC significantly reduce the HD95 score, indicating their ability to capture finer structures and generate more precise contours. This improvement is attributed to the proposed attention function, which effectively harnesses the multi-dimensional information within the input space for computing kernels.Comparative Results on ACDC Dataset. From the results shown in Table 2 , we can observe similar performance improvement trends as on the Synapse dataset. For UNet and TransUNet [5] . Result are obtained from the TransUNet [6] and UNet [38] with different dynamic convolution on the ACDC (above) and Synapse (below) datasets. backbones, DYConv (4×) and CondConv (8×) yield mDICE improvements of 0.59%/0.83% and 1.14%/0.78% over baseline models, respectively. Our method, MAGIC (1×), utilizing a single convolutional kernel, significantly outperforms these, with mDICE improvements of 1.53%/2.05%.Comparative Results on GlaS Dataset. In experiments conducted on the GlaS dataset, our MAGIC also outperforms other dynamic convolutions, as demonstrated in Table 3 . We can see that UNet with our MAGIC (4×) significantly enhances the segmentation accuracy, whereas the use of other dynamic convolutions results in minimal improvement.Comparative Results on Skin Lesion Segmentation Dataset. The comparison results for the ISIC 2017 skin lesion segmentation benchmarks against the existing dynamic convolutions are presented in Table 4 . The experimental results demonstrate that our MAGIC achieves the highest mDICE (84.02%), SE (75.50%), and ACC (92.92%), surpassing TransFuse baseline by 2.50%, 3.19%, and 1.00%, respectively. It's noteworthy that ODConv, despite design an attention mechanism to capture multi-scale kernel space information, falls short by overlooking the synergy between dependencies within the input space, resulting in sub-optimal performance.Analysis of Efficiency. We conduct a quantitative analysis of different dynamic convolution parameters and computational complexity (measured in GFLOPs) using the ResNet 18 backbone, as shown in Table 5 . Specifically, our MAGIC notably improves the expressive power of regular convolution with negligible extra computation cost. Compared to other dynamic convolution methods, our approach stands out for its competitiveness in both parameter quantity and computational cost. Note that our MAGIC (4×) outperforms DDConv (75.29% vs. 74.33%), using only about half as many parameters (49.99M vs. 93.69M). This demonstrates that our MAGIC achieves the best trade-off between segmentation performance and computational complexity. Visualization Results. Figure 3 illustrates the class activation maps generated by UNet/TransUNet employing different dynamic convolutions on the ACDC and Synapse datasets. Our MAGIC, in contrast to other dynamic convolutions, showcases a more precise focus on the segmentation area and provides a finer delineation of the segmentation region. Impressively, we achieve enhanced segmentation quality using only a single convolutional kernel. Furthermore, we present segmentation results of baseline models (UNet and TransFuse) with our MAGIC and other dynamic convolutions on the GlaS and ISIC 2017 datasets, as shown in Figure 4 . Our MAGIC significantly reduces the number of false positive segments compared to other dynamic convolutions. Particularly in segmenting complex targets on the GlaS dataset, our results align more closely with the ground truth, leading to fewer inaccurately segmented areas.Effectiveness of Dimensional-Reciprocal Fusion. To assess the effectiveness of Dimensional-Reciprocal Fusion (DRF), we integrate MAGIC (4×) into the UNet and TransUNet architectures. Table 6 clearly demonstrates the synergistic effect of incorporating different dimensional information for computing kernels. Compared to UNet/TransUNet baselines, Experiment A. 1 shows marginal improvements in mDice scores by 0.51%/0.31%, 0.18%/0.45%, and 0.21%/0.5% on the Synapse, ACDC, and GlaS datasets, respectively. These results highlight the significance of the attention mechanism in dynamic convolution. Analyses of Experiments A. 2, A. 3, and A. 5 reveal that incorporating information from both the channel and spatial dimensions significantly enhances segmentation accuracy. Furthermore, our observations indicate that capturing the global information of the input is essential for enhancing segmentation performance. Therefore, our study validates the assumption that incorporating three distinct dimensional information into kernel computation yields synergistic advantages.Effectiveness of Kernel Recalculation. Table 7 presents the effectiveness of Kernel Recalculation (KR), which learns the correlations among kernels to enhance the aggregation. The outcomes demonstrate that our KR surpasses the vanilla linear combination in four experiments, achieving enhancements in the mDice score ranging from 0.43% to 1.25%. This confirms the assumption that improving the aggregation process aids in comprehending complex patterns within the input features.Effectiveness of Convolutional Kernels Number. Tables 1, 2, 3, and 4 show the effectiveness of the number of convolutional kernels on segmentation accuracy. A clear trend emerges, showing that increasing the number of convolutional kernels results in more precise segmentation of the target. This effect is especially notable in purely convolutional networks. Moreover, our MAGIC showcases remarkable adaptability with Transformer architectures, sustaining strong performance despite the use of a minimal set of convolutional kernels (such as, one or two). This affirms that our MAGIC approach can be used to replace regular convolutions in many architectures. 
conclusion: 
 In this paper, we propose MAGIC, an elegant form of dynamic convolution for medical image segmentation. The key insight is to utilize the rich information within the input for computing multiple convolutional kernels. To further enhance segmentation performance, we employ a non-linear aggregation strategy that effectively leverages the power of multiple kernels. Extensive experiments demonstrate that our MAGIC outperforms existing dynamic convolution methods on four popular medical datasets considerably. 
 
 Appendix:  


Decision: The reviewers agreed that the paper is reasonably novel and the experiments are reasonably convincing for publication. They also agreed that many parts of the paper should be revised to make the novelty and motivation clear, to include more references, and to better explain the method.

Reviews: review: paper_summary: paper_summary: The paper “MAGIC: Rethinking Dynamic Convolution Design for Medical Image Segmentation” introduces a new form of dynamic convolution called MAGIC, which aims to address the problems of computing multiple convolutional kernels on dynamic convolution. Specifically, dimensional-reciprocal fusion module is proposed to capture correlations among input maps. Furthermore, MAGIC contains a kernel recalculation module to enhance the efficiency of aggregation by learning the interaction between kernels. Besides, this paper aiming at the challenge current dynamic convolution faced, logically and rigorously explaining the method MAGIC.
strength_weakness: strength_weakness: S1: The introduction of MAGIC presents an effective approach to consider the impact of other dimensional contexts (spatial information and long-range dependencies) on dynamic convolution, the importance across different kernels is taken into account.

S2: This paper demonstrates the issues that occurred in the previous work, based on these issues, a well-structured illustration of the two ideas (Dimensional-Reciprocal Fusion, Kernel Recalculation) proposed in this paper.
limitations: limitations: W1: The experimental data in this paper is not sufficient to prove validity. As this paper is about medical image segmentation, the CiT-Net[*] conducted experiments on dataset ISIC2018, but this paper is on ISIC2017, and the results of CiT-Net is better. The reviewer considers the results may not clearly demonstrate the method in this paper is more effective for medical image segmentation, experiments based on CiT-Net backbone are required.

W2: Doubts about the purpose of the MAGIC raised by this paper. The title contains “for Medical Image Segmentation”, but this paper is mainly to deal with the dynamic convolution, in the third part, it is not seen that MAGIC is specifically targeted at medical image segmentation. The reviewer is curious about the effect of this method in other downstream tasks, such as object detection, image classification.

W3: The grammar of the paper needs to be carefully checked, there are some article errors in the paper. 

[*] Lei T, Sun R, Wang X, et al. CiT-Net: convolutional neural networks hand in hand with vision transformers for medical image segmentation[J]. arXiv preprint arXiv:2306.03373, 2023.
paper_summary: paper_summary: This paper enhances dynamic convolution through a dimensional-reciprocal fusion module and a kernel recalculation module. Unlike previous works [11, 45] that use the SE module [21], the proposed approach integrates spatial, channel, and non-local information. The kernel recalculation module then assesses the importance of these kernels. Experimental results validate the method's effectiveness.
strength_weakness: strength_weakness: - The paper is well-organized.
- The experiments are comprehensive, and the ablation study highlights the significance of each proposed module.
limitations: limitations: - The design of the kernel recalculation appears to duplicate the attention function. Both \( k \) and \( \alpha \) are generated based on the fusion feature \( \beta \) through a nonlinear operation. Since \( \beta \) already fuses the feature map from multiple channels [11], it contradicts the motivation to introduce \( \alpha \), which seems to have a similar function.
- The meaning of '4x' and '8x' is unclear. The author should explain this notation.
- The Synapse dataset [29] includes 13 organs, but Table 1 only shows results for some of them. Please clarify whether the experiments were conducted on all organs or only a subset.
- There are some typos. In Row 341, the shape of \( \beta_c \) should be \( h \times w \).
paper_summary: paper_summary: The paper proposes a novel dynamic convolution method called "MAGIC" for medical image segmentation tasks. The key contributions are:

Introducing a dimensional-reciprocal fusion module to capture correlations among input maps across spatial, channel, and global dimensions for computing convolutional kernels.

Designing a kernel recalculation module to enhance the efficiency of kernel aggregation by learning the interaction between kernels.

Demonstrating the effectiveness of MAGIC as a drop-in replacement for regular convolution in both pure CNN and hybrid CNN-Transformer backbones.
strength_weakness: strength_weakness: The proposed MAGIC method addresses two key limitations of existing dynamic convolution approaches - sub-optimal computation of convolution kernels and inefficient linear kernel aggregation.

The dimensional-reciprocal fusion and kernel recalculation modules are novel and well-motivated to improve the flexibility and representation capacity of dynamic convolutions.

Extensive experiments on four medical image segmentation benchmarks show MAGIC outperforms regular convolution and existing dynamic convolution methods.

The paper is well-structured, with a clear explanation of the motivations, method details, and empirical evaluation.
limitations: limitations: 1. More detailed implementation specifics would be greatly beneficial to the readers. These specifics could include hyperparameter settings, training procedures, and ablation studies. The ablation studies, in particular, would help to understand the individual contributions of the proposed components within your method.
2. It would be helpful to compare your method with a wider range of state-of-the-art medical image segmentation methods. Currently, the comparison seems to be limited to just regular convolution and existing dynamic convolution methods. A more comprehensive evaluation would provide a broader context for the effectiveness of your method.
3. Discussing potential limitations or failure cases of the MAGIC method would give readers a more balanced perspective. It would be beneficial to know what circumstances might limit the effectiveness of the proposed method.
4. Can you provide more insights into how the dimensional-reciprocal fusion module captures the correlations across spatial, channel, and global dimensions? Understanding the key design choices and intuitions behind this module could help others to further refine or build upon your work.
5. How does the kernel recalculation module enhance the efficiency of kernel aggregation? Can you elaborate on the specific advantages it provides over a simple linear aggregation? This could provide more clarity on the unique benefits offered by your method.
6. What are the computational and memory cost differences between MAGIC and the regular convolution or existing dynamic convolution methods? A quantitative comparison would be helpful. This would provide a clear picture of the trade-offs involved in using your method over other existing ones.
7. Some significant literature could be further included to improve the comprehensiveness of the literature review regarding medical image segmentation and efficient network designs. The current innovation is limited due to the lack of a more detailed literature review of the precedent.

Hu S, Liao Z, Zhang J, et al. Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2022, 42(1): 233-244.
Li C, Lin M, Ding Z, et al. Knowledge condensation distillation[C]//European Conference on Computer 
Vision. Cham: Springer Nature Switzerland, 2022: 19-35.
Sun X, Chen C, Wang X, et al. Gaussian dynamic convolution for efficient single-image segmentation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(5): 2937-2948.
Li C, Ma W, Sun L, et al. Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation[J]. Neural Computing and Applications, 2022: 1-14.
Sun L, Li C, Ding X, et al. Few-shot medical image segmentation using a global correlation network with discriminative embedding[J]. Computers in biology and medicine, 2022, 140: 105067.
Li C, Zhang Y, Liang Z, et al. Consistent posterior distributions under vessel-mixing: a regularization for cross-domain retinal artery/vein classification[C]//2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021: 61-65.
Lei T, Zhang D, Du X, et al. Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network[J]. IEEE Transactions on Medical Imaging, 2022.
paper_summary: paper_summary: This article proposes a dynamic convolution kernel named MAGIC, primarily utilizing DRF and KR modules to capture spatial, channel, and global information, while also considering the importance of the convolution kernel itself. Extensive experiments were conducted on numerous datasets.
strength_weakness: strength_weakness: Excellent innovations, good illustrations, ample comparative experiments, and mature writing skills make me believe this is an outstanding paper.
limitations: limitations: - In the abstract, the authors mention that a drawback of dynamic convolution is "The linear kernel aggregation is inefficient." However, the authors' KR method merely takes into account the importance of the convolution kernel itself, and ultimately also performs linear aggregation.
  
- In Figure 2, I believe the input map processed through [avg, max] pooling results in a shape of [2, H, W], rather than [C, 1, 1] as mentioned in line 343. How is [2, H, W] reshaped to [C, 1, 1] and added to the other attention-extracted tensors?
  
- In line 453, L1/L2 are linear matrices. Are they learnable, or are they consistent with L1/L2 in the Non-Local section? I believe the authors need to provide a clearer description of this part.
  
- Why are different networks used for different datasets? ACDC employs three backbone networks, whereas ISIC uses only one backbone network. To me, such an experimental setup is somewhat insufficient and fails to fully convince me.
  
- In Table 6, it is somewhat unusual to place upward arrows next to the dataset names. Typically, upward and downward arrows should be placed next to the metrics. I believe the authors could revise the table accordingly. Similar issues appear in other tables as well.
  
- In line 847, does "using only a single convolutional kernel" mean (1×)? Moreover, the paper seems to lack an explanation of the meanings of 1×, 2×, and 4×. I suggest adding some basic descriptions.
  
- In line 902, the section "Effectiveness of Convolutional Kernels Number" mentions that in previous comparative experiments, the performance of 1× or 2× convolutional kernels was sometimes better than that of 4× convolutional kernels. This is somewhat counterintuitive and does not align with the authors' explanations. I suggest adding text or experiments to investigate and discuss this phenomenon.
  
- Including inference time metrics in Table 5 would be better.