Zhang Zeyu, Li Bin, Yan Chenyang, Furuichi Kengo, Todo Yuki
Division of Electrical Engineering and Computer Science, Kanazawa University, Kanazawa 9201192, Japan.
Department of Nephrology, Kanazawa Medical University, Kahoku 9200293, Japan.
Biomimetics (Basel). 2025 Jan 8;10(1):34. doi: 10.3390/biomimetics10010034.
Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network's biomimetic performance of human attention. By incorporating matrices generated from shifted images into the self-attention mechanism, the network gains the ability to preemptively acquire information from surrounding regions. Experimental results demonstrate the superior performance of our approaches across various benchmark datasets, validating their effectiveness. Furthermore, the method was applied to patient kidney datasets collected from hospitals for diabetes diagnosis, where they achieved high accuracy with significantly reduced computational demands. This advancement showcases the potential of our methods in the field of biomimetics, aligning well with the goals of developing innovative bioinspired diagnostic tools.
人工智能凭借其卓越的适应性，已逐渐融入日常生活。自注意力机制的出现推动了Transformer架构在各个领域的应用，包括在医学中作为高效精确的诊断和预测工具。为提高准确性，我们提出了双注意力（DA）方法，该方法提高了神经网络对人类注意力的仿生性能。通过将从移位图像生成的矩阵纳入自注意力机制，网络获得了从周围区域抢先获取信息的能力。实验结果表明，我们的方法在各种基准数据集上具有卓越性能，验证了其有效性。此外，该方法应用于从医院收集的患者肾脏数据集进行糖尿病诊断，在显著降低计算需求的情况下实现了高精度。这一进展展示了我们方法在仿生学领域的潜力，与开发创新生物启发诊断工具的目标高度契合。
