Colorectal cancer (CRC) screening is primarily conducted through endoscopy, which remains the gold standard due to its high diagnostic accuracy. However, its invasiveness, operational complexity, and poor patient compliance limit its widespread application. Therefore, intelligent diagnostic approaches based on traditional Chinese medicine (TCM) have gained increasing attention as potential noninvasive alternatives. In this study, an intelligent TCM-assisted diagnostic framework is developed using tongue image analysis for auxiliary CRC diagnosis. Considering the characteristic of limited tongue image samples, the problem is formulated as a few-shot learning task. To address this challenge, a meta-learning-based framework, Meta-Learning with Task-Adaptive Loss and Flexible Affine (MeTALFA), is employed to enhance model adaptability and generalization across tasks. The model integrates task-adaptive loss and dynamic loss-weighting strategies for optimized learning under small-sample conditions. A dataset containing 5,967 tongue images was collected and categorized into healthy and CRC groups. Four meta-learning algorithms, including MAML, MAML++, MeTAL, and MeTALFA, were evaluated under identical experimental settings. Experimental results show that MeTALFA achieves the best diagnostic performance, with an accuracy of$99.67 \% \pm 0.15 {\%}$, precision of 99.57%, recall of 99.77%, and AUC of 98.04%. These findings demonstrate that MeTALFA provides an effective and data-efficient approach for noninvasive CRC screening based on tongue images.

Existing intelligent diagnostic methods in Traditional Chinese Medicine (TCM) heavily rely on clinical experience and rule-based models, which often lack generalizability and overlook medical text data. To address this, we propose a multi-instance multi-label graph neural network model for TCM diagnosis. We frame TCM diagnosis as a multi-instance multilabel problem: each medical record is treated as a bag, where instances are symptom-cluster subgraphs generated via random walks, labeled with syndrome elements (zhengsu). Features are extracted from these sub-instances, aggregated into a bag-level representation, and subsequently mapped to zhengdu labels through a classifier by a graph neural network. Our model achieves a Hamming Loss of 2.58 %, Ranking Loss of 2.56 %, One-Error of 21.55 %, Coverage of 4.565, and Average Precision of 81.55 %, outperforming classic MIML methods including DeepMIML, AttentionMIML, Fast-MIML, and Lnn-MIML across these metrics. These results demonstrate that combining multiinstance multi-label learning with graph neural networks effectively captures symptom-zhengsu relationships, offering a promising direction for intelligent and objective TCM diagnostic research.

Discriminative models, such as Convolutional Neural Networks and Vision Transformers, have driven significant progress in medical image segmentation, their limited generalization ability across diverse datasets remains a key challenge. Consequently, generative models have been increasingly explored for such image-to-image tasks due to their capacity to produce flexible and diverse outputs. Among these, methods based on diffusion models have shown considerable promise. A critical drawback, however, is that existing approaches often fail to adapt these models natively for segmentation, leading to compromised performance. To address this limitation, we introduce MSLD, a novel framework for medical semantic segmentation based on latent diffusion models. Our approach recasts segmentation as a conditional generation problem, where the model is fine-tuned to generate a segmentation mask conditioned on the input image. Extensive experiments on multiple datasets demonstrate that our proposed method achieves high-quality medical image segmentation.

Health and smart healthcare represent a major future trend, in which intelligent traditional Chinese medicine (TCM) plays a significant role. This article reviews recent advances in intelligent assisted diagnosis methods and TCM inspection-based classification tasks. In particular, intelligent TCM inspection diagnosis and its related tasks are the most studied, especially for facial, tongue, eye, and palm diagnoses using the corresponding medical images. Besides, it is mainly divided into body constitution recognition, disease-assisted diagnosis, medical image segmentation tasks by the facial diagnosis and tongue diagnosis. Intelligent TCM assisted diagnostic methods and disease recognition tasks are also included and categorized for overview. From the summary of the reviewed literatures, the main tasks using the medical images are disease classification and recognition, image segmentation, and target detection of disease locations. Finally, the research trends of intelligent TCM four diagnoses and their combination diagnosis are discussed, which can be regarded as the tasks of multi-classification, multi-label, multi-modal fusion, and multi-tasking from the perspective of problem modeling.

According to the traditional Chinese medicine composite constitution theory, body constitution recognition is modeled as a unique task of multi-label problems using tongue images. Although MLP-like architecture is one of the base models except for CNN-based, Transformer-based, and MLP-like-based for computer vision tasks, the performance of the MLP-like-based architecture for the multi-label BCR task needs to expand for multi-label BCR task with a specific module using tongue images. Thus, a novel dual transform MLP-like (DT-MLP) model is designed, in which discrete Fourier transform and chaos transform are used for feature extraction of their branches. Besides, a new multi-label tongue body constitution dataset is constructed. The results demonstrate that the proposed DT-MLP outperforms other state-of-the-art MLP-like models on mAP, AUC, and Acc, respectively.

Effectively tuning the parameters of proportional–integral–derivative (PID) controllers has persistently posed a challenge in control engineering. This study proposes enhanced hippopotamus optimization (EHO) to address this challenge. Latin hypercube sampling and adaptive lens reverse learning are used to initialize the population to improve population diversity and enhance global search. Additionally, an adaptive perturbation mechanism is introduced into the position update in the exploration phase. To validate the performance of EHO, it is benchmarked against hippopotamus optimization and four classical or state-of-the-art intelligent algorithms using the CEC2022 test suite. The effectiveness of EHO is further evaluated by applying it in tuning PID controllers for different types of systems. The performance of EHO is compared with five other algorithms and the classical Ziegler–Nichols method. Analysis of convergence curves, step responses, box plots, and radar charts indicates that EHO outperforms the compared methods in accuracy, convergence speed, and stability. Finally, EHO is used to tune the cascade PID controller for trajectory tracking in a quadrotor unmanned aerial vehicle to assess its applicability. The simulation results indicate that the integrals of the time absolute error for the position channels ( x, y, z ), when the system is optimized using EHO over an 80 s runtime, are 59.979, 22.162, and 0.017, respectively. These values are notably lower than those obtained by the original hippopotamus optimization and manual parameter adjustment.

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.

Medical images such as facial and tongue images have been widely used for intelligence-assisted diagnosis, which can be regarded as the multi-label classification task for disease location (DL) and disease nature (DN) of biomedical images. Compared with complicated convolutional neural networks and Transformers for this task, recent MLP-like architectures are not only simple and less computationally expensive, but also have stronger generalization capabilities. However, MLP-like models require better input features from the image. Thus, this study proposes a novel convolution complex transformation MLP-like (CCT-MLP) model for the multi-label DL and DN recognition task for facial and tongue images. Notably, the convolutional Tokenizer and multiple convolutional layers are first used to extract the better shallow features from input biomedical images to make up for the loss of spatial information obtained by the simple MLP structure. Subsequently, the Channel-MLP architecture with complex transformations is used to extract deep-level contextual features. In this way, multi-channel features are extracted and mixed to perform the multi-label classification of the input biomedical images. Experimental results on our constructed multi-label facial and tongue image datasets demonstrate that our method outperforms existing methods in terms of both accuracy (Acc) and mean average precision (mAP).