Liu Feng, Wang Zheng, Li Baotian, Wang Decai, Liu Mingyu, Gou Fangfang, Wu Jia
School of Information Engineering, Shandong Youth University of Political Science, Jinan, China.
New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan, China.
Sci Rep. 2025 Aug 25;15(1):31269. doi: 10.1038/s41598-025-17369-4.
Digital pathology has revolutionized cancer diagnosis through microscopic analysis, yet manual interpretation remains hindered by inefficiency and subjectivity. Existing deep models for osteosarcoma cell nucleus recognition suffer from the difficulty of capturing hierarchical relationships in single-dimensional attention mechanisms, leading to inaccurate edge recognition. Furthermore, the fixed receptive field of CNNs limits the aggregation of multi-scale information, hindering the differentiation of overlapping cells. This study introduces MACC-Net, a novel multi-attention based method designed to enhance the recognition accuracy of digital pathology images. By integrating channel, spatial, and pixel-level attention mechanisms, MACC-Net overcomes the limitations of traditional single-dimensional attention models, improving feature consistency and receptive field expansion. Experimental results demonstrate a Dice Similarity Coefficient (DSC) of 0.847, highlighting MACC-Net's potential as a reliable auxiliary diagnostic tool for pathologists. Code: https://github.com/GFF1228/MACCNet .
数字病理学通过微观分析彻底改变了癌症诊断，但人工解读仍因效率低下和主观性而受到阻碍。现有的骨肉瘤细胞核识别深度模型在捕捉单维注意力机制中的层次关系方面存在困难，导致边缘识别不准确。此外，卷积神经网络（CNN）的固定感受野限制了多尺度信息的聚合，阻碍了重叠细胞的区分。本研究引入了MACC-Net，这是一种基于多注意力的新颖方法，旨在提高数字病理学图像的识别准确性。通过整合通道、空间和像素级注意力机制，MACC-Net克服了传统单维注意力模型的局限性，提高了特征一致性并扩大了感受野。实验结果表明，其Dice相似系数（DSC）为0.847，凸显了MACC-Net作为病理学家可靠辅助诊断工具的潜力。代码：https://github.com/GFF1228/MACCNet 。
