MTAL: A Novel Chinese Herbal Medicine Classification Approach with Mutual Triplet Attention Learning
Wangli Hao, Meng Han, Shancang Li, Fuzhong Li
Abstract
Chinese herbal medicine classification is a critical task in medication distribution and intelligent medicine, as well as a significant topic in computer vision. However, the majority of contemporary mainstream techniques are semiautomatic, with low efficiency and performance. To tackle this problem, a novel Chinese herbal medicine classification approach, Mutual Triplet Attention Learning (MTAL), is proposed. The motivation of our approach is to leverage a group of student networks to learn collaboratively and teach each other about cross‐dimension dependencies throughout the training process, with the goal of quickly gaining strong feature representations and improving the outcomes. The results of the experiments show that MTAL outperforms other models in terms of accuracy and computation time. MTAL, in particular, improves accuracy by over 5.5 percent while reducing calculation time by over 50 percent.