Artificial intelligence has shown great promise in noninvasive recognition of vulnerable coronary plaques. However, practical data issues in multicenter studies, such as inconsistent data distribution and insufficient or missing data labels, could significantly affect the recognition accuracy. Unsupervised domain adaptation (UDA) can be introduced to address this challenge, but several limits still remain. First, many existing UDA models are black boxes, hindering healthcare professionals' ability to interpret and trust the model's decision. Second, some methods use pseudolabel to enhance performance, but often overlook the quality assessment of these pseudolabels, potentially leading to negative knowledge transfer. To this end, based on the interpretable Takagi–Sugeno–Kang fuzzy system (TSK-FS), a novel domain adaptive method is proposed to improve model generalizability for vulnerable coronary plaques recognition in multicenter data. First of all, TSK-FS is employed to construct a shared fuzzy feature space for the source domain and the target domain, aiming to better align data distribution. To make full use of the information of unlabeled target domain data and further reduce the negative knowledge transfer, the enhanced pseudolabel learning mechanism is further introduced by combining the graph-based random walking and label filtering. Moreover, Multicenter data of 910 patients with suspected or diagnosed coronary artery disease were collected from three hospitals for experiments. Experimental results demonstrate that the proposed DA-TSK-PLR-FS achieves the promising generalizability across multicenter datasets

The first-order Takagi-Sugeno-Kang (TSK) fuzzy classifier with a fully combined fuzzy rule base (FuCo-FRB) is a potent and interpretable classifier for multiple input and multiple output (MIMO) tasks. However, FuCo-FRB possess an exponential increase in the number of fuzzy rules, and it poses challenges for efficient identification of the parameter matrix in MIMO tasks. A lightweight TSK fuzzy classifier (LW-TSK-FC) is proposed to achieve the balance of training efficiency and predictive performance especially on MIMO tasks. It has the following advantages: (1) An adaptive weighting method based on directly connected FRB enables the efficient generation of fuzzy rules, meanwhile retaining the distribution characteristic of FuCo-FRB to reduce information loss. (2) The consequent network of first-order TSK is optimized to a novel series structure by using matrix factorization. This new series structure increases the depth of consequent network and enable the implementation of kernel function and least learning machine (LLM), enhancing the calculation efficiency and predictive performance. (3) There are only weight parameters in consequent network of LW-TSK-FC and these parameters can be identified quantitatively by LLM, leading to a great training efficiency. The comparison experiments with other 14 power classifiers on 13 public UCI datasets shown comparable predictive performance and outstanding training and testing efficiency in both MISO and MIMO tasks. The experiments on a real-world clinical task demonstrated the significant capability of LW-TSK-FC on handling imbalanced small data, and can provide the semantic interpretability for reasoning process.

Interpretable artificial intelligence (AI), also known as explainable AI, is indispensable in establishing trustable AI for bench-to-bedside translation, with substantial implications for human well-being. However, the majority of existing research in this area has centered on designing complex and sophisticated methods, regardless of their interpretability. Consequently, the main prerequisite for implementing trustworthy AI in medical domains has not been met. Scientists have developed various explanation methods for interpretable AI. Among these methods, fuzzy rules embedded in a fuzzy inference system (FIS) have emerged as a novel and powerful tool to bridge the communication gap between humans and advanced AI machines. However, there have been few reviews of the use of FISs in medical diagnosis. In addition, the application of fuzzy rules to different kinds of multimodal medical data has received insufficient attention, despite the potential use of fuzzy rules in designing appropriate methodologies for available datasets. This review provides a fundamental understanding of interpretability and fuzzy rules, conducts comparative analyses of the use of fuzzy rules and other explanation methods in handling three major types of multimodal data (i.e., sequence signals, medical images, and tabular data), and offers insights into appropriate fuzzy rule application scenarios and recommendations for future research.

Stacking-based takagi-sugeno-kang (TSK) fuzzy system ensemble has been successfully applied to imbalanced data classification. However, there still exist many challenges that need to be further addressed. For example, during stacking, augmenting output variables into the input feature space reduces the interpretability of antecedents of fuzzy rules. During sampling for balancing, discovering informative samples usually only relies on training samples, which may reduce generalizability. More importantly, there is no theory to support the reliability of stacking. To address the aforementioned challenges, in this study, we propose a deep reconciled and self-paced TSK fuzzy system ensemble framework termed D-RSP-TSKE for imbalanced data classification. Compared with the existing ensemble frameworks, its superiorities can be exhibited from the following three aspects. First, in the first layer, we use random undersampling to generate a class-balanced training set to train an initial zero-order TSK fuzzy classifier. Based on the TSK fuzzy classifier, then we define classifier-specific and testing-compatible sample sensitivity to discover informative (high-sensitive) samples and design a reconciled and self-paced sampling approach to balance the minority class for the training of the following layers. Second, to improve the interpretability of antecedents of fuzzy rules, we propose to transfer the output variables from antecedents to consequents through equivalent mathematical transformations while keeping the final output unchanged. These transferred output variables are interpreted as the dynamic fuzzy rule confidence. Third, furthermore, we engage in a comprehensive theoretical examination of our stacking-based ensemble to elucidate the underlying mechanisms that enable the stacking strategy to consistently deliver superior performance. We conduct tests and comparisons on 7 artificial datasets and 30 real-world datasets to evaluate D-RSP-TSKE. The experimental results demonstrate the effectiveness and interpretability of D-RSP-TSKE for imbalanced data classification.

Recently, deep learning has been demonstrated to be feasible in eliminating the use of gadoliniumbased contrast agents (GBCAs) through synthesizing gadolinium-free contrast-enhanced MRI (GFCE-MRI) from contrast-free MRI sequences, providing the community with an alternative to get rid of GBCAs-associated safety issues in patients. Nevertheless, generalizability assessment of the GFCE-MRI model has been largely challenged by the high inter-institutional heterogeneity of MRI data, on top of the scarcity of multi-institutional data itself. Although various data normalization methods have been adopted to address the heterogeneity issue, it has been limited to single-institutional investigation and there is no standard normalization approach presently. In this study, we aimed at investigating generalizability of GFCE-MRI model using data from seven institutions by manipulating heterogeneity of MRI data under five popular normalization approaches. Three state-of-the-art neural networks were applied to map from T1-weighted and T2-weighted MRI to contrast-enhanced MRI (CE-MRI) for GFCE-MRI synthesis in patients with nasopharyngeal carcinoma. MRI data from three institutions were used separately to generate three uni-institution models and jointly for a tri-institution model. The five normalization methods were applied to normalize the data of each model. MRI data from the remaining four institutions served as external cohorts for model generalizability assessment. Quality of GFCE-MRI was quantitatively evaluated against ground-truth CE-MRI using mean absolute error (MAE) and peak signal-to-noise ratio(PSNR). Results showed that performance of all uni-institution models remarkably dropped on the external cohorts. By contrast, model trained using multi-institutional data with Z-Score normalization yielded the best model generalizability improvement.

Radiomics, dosiomics and contouromics have been combined to predict adaptive radiotherapy eligibility in nasopharyngeal carcinoma. However, the commonly-used feature concatenation ignores the complementary or consistency relationship across different omics feature spaces. Also, the number of features increases with the concatenation of omics, leading to the curse of dimensionality and potential overfitting. To address the issues, in this study, multi-omics collaborative learning MOCL is developed. In MOCL, a priori knowledge-driven consistency regularization associated with Shannon entropy is designed to automatically explore the weighting consistency across different omics feature spaces. In addition, a label soften strategy is adopted to enlarge the margins between different classes, rendering more freedom for the models to fit the soft label matrix. To avoid overfitting, we design a regularized term deduced from manifold learning to keep samples in the label space as close as possible if they are in the same manifold in the feature space. Experimental results on 311 nasopharyngeal carcinoma patients collected from the Hong Kong Queen Elizabeth Hospital demonstrate the promising performance of MOCL.

The accurate prediction of adaptive radiation therapy (ART) for nasopharyngeal carcinoma (NPC) patients before radiation therapy (RT) is crucial for minimizing toxicity and enhancing patient survival rates. Owing to the complexity of the tumor micro-environment, a single high-resolution image offers only limited insight. Furthermore, the traditional softmax-based loss falls short in quantifying a model’s discriminative power. To address these challenges, we introduce a supervised multi-view contrastive learning approach with an additive margin (MMCon). For each patient, we consider four medical images to form multi-view positive pairs, which supply supplementary information and bolster the representation of medical images. We employ supervised contrastive learning to determine the embedding space, ensuring that NPC samples from the same patient or with the same labels stay in close proximity while NPC samples with different labels are distant. To enhance the discriminative ability of the loss function, we incorporate a margin into the contrastive learning process. Experimental results show that this novel learning objective effectively identifies an embedding space with superior discriminative abilities for NPC images.