Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型

姜舒; 陈琨; 丁卫平; 周天奕; 朱越

doi:10.11959/j.issn.2096-6652.202520

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Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型

学术论文 | 更新时间：2025-07-21

- Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型
- Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention
- 智能科学与技术学报 2025年7卷第2期页码：221-233
- 作者机构：
  
  1.南通大学人工智能与计算机学院，江苏南通 226019
  2.南通大学张謇学院，江苏南通 226019
- 作者简介：
  
  [ "姜舒（1993- ），女，博士，南通大学人工智能与计算机学院讲师，主要研究方向为深度学习、大数据分析等。" ]
  [ "陈琨（2004- ），男，南通大学张謇学院本科生，主要研究方向为深度学习、模糊学习和图像处理等。" ]
  [ "丁卫平（1979- ），男，博士，南通大学人工智能与计算机学院教授、博士生导师，主要研究方向为多模态机器学习、多粒度计算、演化计算和医学大数据分析等。" ]
  [ "周天奕（2000- ），男，南通大学人工智能与计算机学院硕士生，主要研究方向为模糊集、深度学习等。" ]
  [ "朱越（2004- ），男，南通大学张謇学院本科生，主要研究方向为深度学习、粗糙集等。" ]
- 基金信息：
  
  国家重点研发计划项目(2024YFE0202700);国家自然科学基金项目(62406153);国家级大学生创新创业训练计划项目(202410304067Z);江苏省自然科学基金项目(20231337);江苏省高等学校自然科学研究面上项目(23KJB520031;24KJB520032)
- DOI：10.11959/j.issn.2096-6652.202520
  中图分类号： TP391
- 收稿日期：2025-02-12，
  
  修回日期：2025-04-21，
  
  纸质出版日期：2025-06-15
- 稿件说明：
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姜舒,陈琨,丁卫平等.Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型[J].智能科学与技术学报,2025,07(02):221-233.

JIANG Shu,CHEN Kun,DING Weiping,et al.Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):221-233.
姜舒,陈琨,丁卫平等.Axial-FNet：基于模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型[J].智能科学与技术学报,2025,07(02):221-233. DOI： 10.11959/j.issn.2096-6652.202520.

JIANG Shu,CHEN Kun,DING Weiping,et al.Axial-FNet: skin cancer image segmentation model based on fuzzy convolution combined with gated axial self-attention[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):221-233. DOI： 10.11959/j.issn.2096-6652.202520.

摘要

皮肤癌图像分割是医学图像处理领域中的一项关键任务，现常用的分割算法在进行诊断时，无法很好地平衡局部细节信息和全局上下文信息的计算资源请求量。此外，肿瘤边界模糊且难以正确识别分割的问题也亟待解决。针对上述问题，提出了模糊卷积结合门控轴向自注意力的皮肤癌图像分割模型Axial-FNet，该模型由门控轴向自注意力分支和模糊卷积神经网络分支构成。在门控轴向自注意力分支的尾部，设置了门控权重控制器，以控制捕捉局部细节信息和全局上下文信息的比例和程度；融合模糊学习模块至卷积神经网络（CNN）中形成模糊神经网络分支，提取图像局部信息。该模型在降低计算量的同时提升了分割精确度。在ISIC 2017数据集和ISIC 2018数据集上的平均交并比（MIoU）、F1分数（F1-score）和准确率（Accuracy）分别达到了74.23%、83.05%和92.89%，80.91%、88.13%和93.10%。实验结果表明，Axial-FNet较其他多个先进分割模型而言，分割的精确度和可靠性更佳。

Abstract

The task of skin cancer image segmentation is a key task in the field of medical image processing. The commonly used segmentation algorithms can't well balance the computational resource requirements of local information and global context information when performing diagnosis. In addition

the problem of fuzzy tumor boundaries and difficulty in correctly identifying segmentation is also an urgent problem to be solved. Aiming at the above problems

a skin cancer image segmentation model Axial-FNet based on fuzzy convolution combined with gated axial self-attention was proposed. The model was composed of a gated axial self-attention branch and a fuzzy convolutional neural network branch. At the end of the gated axial self-attention branch

a gated weight controller was set to control the proportion and degree of capturing local information and global context information. The fuzzy learning module was fused into the convolutional neural network (CNN) to form a fuzzy neural network branch to extract the local information of the image. The segmentation accuracy was improved by the model while reducing the amount of calculation. The performance of the Axial-FNet model was evaluated on the ISIC 2017 dataset

achieving scores of 74.23%

83.05%

and 92.89% for MIoU

F1-score

and accuracy

respectively

as well as 80.91%

88.13%

and 93.10% for the same metrics on the ISIC 2018 dataset. The experimental results show that Axial-FNet has better segmentation accuracy and reliability than other advanced segmentation models.

关键词

Keywords

references

AHMEDIN JEMAL DVM P. Cancer statistics, 2018[J]. CA: A Cancer Journal for Clinicians, 2018, 68(1): 7-30.

WU H S, CHEN S H, CHEN G L, et al. FAT-Net: feature adaptive transformers for automated skin lesion segmentation[J]. Medical Image Analysis, 2022, 76: 102327.

KHAN M A, SHARIF M I, RAZA M, et al. Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection[J]. Expert Systems, 2022, 39(7): e12497.

RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015. Cham: Springer International Publishing, 2015: 234-241.

ZHOU Z W, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. UNet++: a nested U-Net architecture for medical image segmentation[C]//Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer International Publishing, 2018: 3-11.

XIAO X, LIAN S, LUO Z M, et al. Weighted res-UNet for high-quality retina vessel segmentation[C]//Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME). Piscataway: IEEE Press, 2018: 327-331.

IGLOVIKOV V, SHVETS A, BUSLAEV A V, et al. TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation[EB]. 2018.

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J ] . Advances in Neural Information Processing Systems, 2017, 30 .

CHEN J N, MEI J R, LI X H, et al. TransUNet: rethinking the U-Net architecture design for medical image segmentation through the lens of transformers[J]. Medical Image Analysis, 2024, 97: 103280.

CORDONNIER J B, LOUKAS A, JAGGI M. Multi-head attention: collaborate instead of concatenate[EB]. 2020.

CHEN Y F, ZOU B F, GUO Z X, et al. SCUNet: swin-UNet and CNN bottleneck hybrid architecture with multi-fusion dense skip connection for pulmonary embolism CT image segmentation[C]//Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2024: 7744-7752.

LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2021: 9992-10002.

VALANARASU J M J, OZA P, HACIHALILOGLU I, et al. Medical transformer: gated axial-attention for medical image segmentation[C]//Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. Cham: Springer International Publishing, 2021: 36-46.

SABOUR S, VORA S, DUCKWORTH D, et al. RobustNeRF: ignoring distractors with robust losses[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2023: 20626-20636.

LEE H J, KIM J U, LEE S M, et al. Structure boundary preserving segmentation for medical image with ambiguous boundary[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 4816-4825.

GUAN C, WANG S L, LIEW A W. Lip image segmentation based on a fuzzy convolutional neural network[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(7): 1242-1251.

DENG Y, REN Z Q, KONG Y Y, et al. A hierarchical fused fuzzy deep neural network for data classification[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(4): 1006-1012.

DING W P, WANG H P, HUANG J S, et al. FTransCNN: fusing transformer and a CNN based on fuzzy logic for uncertain medical image segmentation[J]. Information Fusion, 2023, 99: 101880.

IBTEHAZ N, RAHMAN M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020, 121: 74-87.

YAN X Y, TANG H, SUN S L, et al. AFTer-UNet: axial fusion transformer UNet for medical image segmentation[C]//Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2022: 3270-3280.

RUAN J C, XIE M Y, GAO J S, et al. EGE-UNet: an efficient group enhanced UNet for skin lesion segmentation[C]//Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023. Cham: Springer Nature Switzerland, 2023: 481-490.

DOSOVITSKIY A. An image is worth 16x16 words: transformers for image recognition at scale[EB]. 2020.

ZHENG S X, LU J C, ZHAO H S, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 6881-6890.

XIE E Z, WANG W H, YU Z D, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[J]. Advances in Neural Information Processing Systems, 2021, 34: 12077-12090.

CAO H, WANG Y Y, CHEN J N, et al. Swin-unet: unet-like pure transformer for medical image segmentation[EB]. 2021.

LI C, CUI Y, LUO N, et al. Trans-ResNet: integrating transformers and CNNs for Alzheimer's disease classification[C]//Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Piscataway: IEEE Press, 2022: 1-5.

BELLO I, ZOPH B, LE Q, et al. Attention augmented convolutional networks[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2019: 3286-3295.

CHEN B Z, LIU Y S, ZHANG Z, et al. TransAttUnet: multi-level attention-guided U-Net with transformer for medical image segmentation[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(1): 55-68.

JIANG Q, JIN X, CUI X H, et al. A lightweight multimode medical image fusion method using similarity measure between intuitionistic fuzzy sets joint Laplacian pyramid[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(3): 631-647.

NAN Y, DEL SER J, TANG Z Y, et al. Fuzzy attention neural network to tackle discontinuity in airway segmentation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(6): 7391-7404.

崔文成, 王可丽, 邵虹. 基于稠密块和注意力机制的肺部病理图像异常细胞分割[J]. 智能科学与技术学报, 2023, 5(4): 525-534.

CUI W C, WANG K L, SHAO H. Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 525-534.

HAN K, WANG Y H, CHEN H T, et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(1): 87-110.

HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 770-778.

刘霞, 王迪. 深度ReLU神经网络的万有一致性[J]. 中国科学: 信息科学, 2024, 54(3): 638-652.

LIU X, WANG D. Universal consistency of deep ReLU neural networks[J]. Science China Information Sciences, 2024, 54(3): 638-652.

IOFFE S, SZEGEDY C, PARANHOS L, et al. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB]. 2015.

DING W P, GENG S, WANG H P, et al. FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation[J]. Information Fusion, 2024, 112: 102540.

周天奕, 丁卫平, 黄嘉爽, 等. 模糊逻辑引导的多粒度深度神经网络[J]. 模式识别与人工智能, 2023, 36(9): 778-792.

ZHOU T Y, DING W P, HUANG J S, et al. Fuzzy logic guided deep neural network with multi-granularity[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 778-792.

赵亮, 李梦威, 郑玉卿, 等. 二氧化氮浓度时空预测: 一种区间二型直觉模糊神经网络方法[J]. 智能科学与技术学报, 2024, 6(2): 253-261.

ZHAO L, LI M W, ZHENG Y Q, et al. Temporal and spatial prediction of nitrogen dioxide concentration: an interval type Ⅱ intuitionistic fuzzy neural network method[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(2): 253-261.

CODELLA N C F, GUTMAN D, CELEBI M E, et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)[C]//Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Piscataway: IEEE Press, 2018: 168-172.

CODELLA N, ROTEMBERG V, TSCHANDL P, et al. Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC)[EB]. 2019.

OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: learning where to look for the pancreas[EB]. 2018.

WANG H, CAO P, WANG J, et al. Uctransnet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2022, 36(3): 2441-2449.

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