Chaos-MLP: Chaotic Transform MLP-Like Architecture for Medical Images Multi-Label Recognition Task.

Abstract

The theory of "three-stage prevention" in view of the body constitution is the key technology of modern Chinese medicine for "Preventive Treatment of Diseases". In particular, automated body constitution recognition (BCR) is an integral part of intelligent Traditional Chinese Medicine (TCM), which is extremely valuable for disease prevention and diagnosis. Actually, BCR is a challenging multi-label recognition task by the TCM composite constitution theory. First, two new databases are constructed, one is a multi-label facial body constitution (MFBC), and another is a multi-label tongue body constitution (MTBC). Second, a novel MLP-like architecture, named Chaos-MLP, is designed for the BCR task, which interacts with the channel chaotic features of extracted medical images and fuses them with the width and height channel direction features, respectively. Notably, the chaotic transform can enhance the distinguishability of extracted features from the medical images. Moreover, we propose a binary center cognitive gravity loss (BCCGL) to enhance the learning ability of the Chaos-MLP for unbalanced body constitution labels. Our proposed method shows superior performance on both MFBC and MTBC datasets than other state-of-the-art (SOTA) MLP-like networks and a vision graph-based neural network (VGNN), which include Wave-MLP, Cycle-MLP, Vip, and Active-MLP.