一种轻量级CNN集成紧凑卷积Transformer，用于多尺度特征学习并降低乳腺癌钼靶图像检测与分类的计算复杂度

中文摘要 AI 辅助

多年来，卷积神经网络（CNN）在利用医学图像进行癌症检测和分类方面展现出强大的能力。然而，基于CNN的模型往往难以捕获长距离上下文依赖关系。在此类场景中，在CCT层之后集成紧凑卷积Transformer（CCT）架构，使得CNN提取的特征能够通过CCT分词器重塑为紧凑的补丁令牌，随后添加位置嵌入以保留空间结构。使用5折交叉验证，该模型在3组乳腺癌钼靶图像上进行了测试。仅用250,435个参数，模型在3个数据集上达到了99%-100%的准确率，表明其具有稳健的泛化能力。可解释人工智能（XAI）被集成到模型中，以解释乳腺癌分类过程，从而增强临床信任。结果表明，所提出的框架适用于计算机辅助诊断系统，尤其是在资源受限的临床环境中。所提出的CNN集成CCT的新颖之处在于，通过将卷积分词与基于Transformer的学习相结合，克服了CNN在最后几层中梯度退化的局限性。该模型比ViT更轻量，而ViT在捕获长距离依赖方面有效，该模型通过捕获乳腺组织区域之间的长距离依赖关系，也已被证明在乳腺癌分类中是高效的。

英文摘要

Over the years, Convolutional Neural Networks (CNNs) have demonstrated strong capability in cancer detection and classification using medical images. However, CNN-based models often struggle to capture long-range contextual dependencies. In such scenarios, integrating Compact Convolutional Transformer (CCT) architectures after the CCT layer allows CNN-extracted features to reshape into compact patch tokens using a CCT tokenizer, followed by the addition of positional embeddings to preserve spatial structure. Using 5-fold cross-validation, the model was tested on 3 sets of breast cancer mammography. With only 250,435 parameters, the model achieved 99%-100% accuracy across 3 datasets, indicating robust generalization. Explainable AI (XAI) was integrated into the model to explain the breast cancer classification process to enhance clinical trust. The results indicate that the proposed framework is suitable for computer-aided diagnosis systems, particularly in resource-constrained clinical environments. The novelty of the proposed CNN-integrated CCT overcomes the limitation of CNN's gradient degradation in the last layers by integrating convolutional tokenization with transformer-based learning. Lighter than ViT, which is effective in capturing long-range dependencies, the model has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.

发表机构

• North South University（南北大学）

机构由 AI 辅助整理，请以论文原文为准。