This study proposes a novel method for extracting breast cancer tumor heterogeneity descriptors to non-invasively predict whether pathological complete response (pCR) can be achieved after neoadjuvant chemotherapy (NAC). These localized descriptors extract corresponding heterogeneity features for different radiomic features and are able to capture tumor characteristics at various localization levels. These descriptors also capture tumor heterogeneity both at the individual tumor level and across the whole dataset, providing decision-making models with features that are both more effective and interpretable. We validated the effectiveness of the proposed features with the Kolmogorov-Arnold network (KAN) across multiple centers, yielding an AUC of 0.92 when combined with pathological features and demonstrating good performance in external datasets (AUCs of 0.84 and 0.81). Additionally, we transform the best model into a symbolic formula to intuitively explain the machine learning model's prediction process, showing how factors such as age, HER2, Ki-67 and heterogeneity influence the prediction. The symbolized model is consistent with the experience of clinical experts, which enhances users' confidence in deep models. The experimental results show that our proposed features and method outperform classical heterogeneity features and end-to-end neural networks with a small additional computational cost.

Small models, despite their computational efficiency for real-time and edge applications, remain vulnerable to adversarial attacks. Adversarial Distillation (AD) has proven effective in enhancing model robustness, yet current approaches predominantly rely on single-strategy adversarial samples with either fixed or adaptive teacher supervision. Fixed supervision often leads to over-smoothing due to static guidance, while adaptive supervision incurs higher computational costs and convergence instability. To address these limitations, we propose Dual Adversarial Distillation (DAD), a novel framework that synergistically combines fixed and adaptive supervision through multi-intensity adversarial samples, with customized distillation strategies designed to enhance knowledge diversity and feature transfer. For fixed supervision, a Mixup-based strategy is employed to diversify teacher knowledge, ensuring robust feature representations by aligning the student model with the teacher's rich representations through cross-strength feature consistency. For adaptive supervision, learning diversity is augmented by integrating transformed features from multiple projection modules, effectively minimizing the feature distribution gap between teacher and student models. Computational efficiency is optimized through variable iteration strategies. Extensive experiments demonstrate the effectiveness of our method in improving the robustness of small models, achieving state-of-the-art baselines.

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The recognition and segmentation of the inflamed appendix are important for AA diagnosis. However, it is a challenging task to find and segment the inflamed appendix from computed tomography (CT) images due to the varying sizes and shapes of different appendices and blurred borders with nearby tissues. To the best of our knowledge, the general expert segmentation model suffers due to the characterization of the inflamed appendix. Thus, we propose a deformable convolutional encoder-decoder network (DCED) for better recognition and segmentation of the inflamed appendix. The network consists of an encoder, a bottleneck, and a decoder. The encoder is composed of several convolutional neural network (CNN) layers to capture the local structural information. The bottleneck based on a vision transformer (ViT) focuses on the region of interest (ROI) using the global attention mechanism. The encoder and bottleneck modules effectively combine the local and global information of input data to locate the inflamed appendix. The decoder based on a deformable convolutional network (DCN) learns the varied boundary information, which helps to improve the accuracy of boundary segmentation. Extensive experimental results on a real-world AA dataset show that the proposed method yields the best average Dice similarity coefficient (DSC) of 71.29% and average Hausdorff Distance 95% (HD95) of 12.38 mm in comparison to state-of-the-art segmentation methods.

Graph contrastive learning (GCL) is emerging as a pivotal technique in graph representation learning. However, recent research indicates that GCL is vulnerable to adversarial attacks, while existing robust GCL methods against adversarial attacks are inefficient and lack scalability due to the significant computational expenses of explicit adversarial attacks on the graph structure. To address the shortcomings of existing approaches, we propose an efficient and robust GCL via multiadversarial views training framework, called ERMAV. Specifically, the ERMAV generates two adversarial views by attacking both node attributes and latent representations on randomly sampled subgraphs. The method conducts explicit adversarial attacks on node attributes by attacking node attributes and implicit adversarial attacks on the graph structure by attacking latent representations, which avoids the costly computation of explicit graph structure attacks. Moreover, two efficient attack methods are developed to construct adversarial perturbations, which can dynamically generate different adversarial views to enhance sample diversity in the training phase. Furthermore, to validate the effectiveness and robustness of the proposed framework, extensive experiments of node classification on seven real-world datasets are conducted. Experimental results show that our ERMAV outperforms state-of-the-art GCL methods on the original graphs and is consistently more robust than existing robust GCL methods on a variety of attacked graphs. This demonstrates the strong robustness and great potential of our ERMAV in real-world applications.

In this work, we propose a radiomics-guided neural network, XRadNet, for breast cancer molecular subtype prediction. XRadNet is a two-head neural network, with one for predicting molecular subtypes and the other for approximating radiomic features. In addition, a training scheme with radiomics guidance is proposed to improve performance. First, we conduct a series of experiments to test the radiomic feature learning capacity of different neural networks, which determines the backbone of XRadNet. Moreover, significant radiomic features are also determined according to radiomics and prior knowledge. XRadNet is subsequently pretrained in a self-supervised manner. The pretraining uses synthetic samples to train the backbone and radiomic feature regression head. This mitigates the impact of an insufficient number of samples. Finally, XRadNet is fine-tuned with a downstream real-world dataset by enabling all heads. Furthermore, a logistic regression is built with radiomic features and learned features, which provides a new way to interpreting the trained model with concepts familiar to radiologists. The experimental results show that XRadNet effectively predicts the four molecular subtypes of breast cancer. These results also demonstrate that the proposed training scheme yields better or competitive performance than those models pretrained on ImageNet or medical datasets.

Self-knowledge distillation, abbreviated as SKD, exhibits greater computational efficiency than traditional knowledge distillation (KD) because it learns from its own predictions rather than from a pretrained teacher. Existing SKD methods diversify knowledge through auxiliary branches, data augmentation, historical models, and label smoothing. However, previous methods primarily extract knowledge from a single-source teacher, overlooking the diversity and complementarity of various types of teacher knowledge in model learning, thereby limiting performance improvements. In response to this challenge, we propose a pioneering paradigm termed multisource teacher collaboration for self-knowledge distillation (MSTCS-KD), which integrates knowledge from diverse types of teachers to complementarily enhance the model's learning capability. We start by adding lightweight auxiliary branches with different structures in the shallow layers to build the student network, while also incorporating a teacher-guided attention mechanism to support adaptive learning. Then, we perform collaborative distillation by combining "heterogeneous knowledge" from the primary network's deepest layers with "homogeneous knowledge" from the student's outputs on augmented samples. This complementary distillation approach improves the model's ability to learn features, generalize, and enhance trainability. Extensive experiments demonstrate that our method outperforms other state-of-the-art SKD methods across various network architectures and datasets.

When analyzing CT images of patients with suspected appendicitis, radiologists need to observe and examine consecutive 2D CT slices. Computer-assisted detection of the appendix in 2D CT slices significantly improve the diagnostic efficiency of radiologists. However, existing 2D medical image object detection methods primarily focus on spatial features within a single CT slice, which overlook spatial relationships between consecutive slices. We propose an Appendix Object Correction Network (AOCN) to refine predictions of universal object detectors. Although AOCN is a 2D network, it effectively leverages spatial relationships across consecutive CT slices. AOCN requires only a few training epochs to improve the accuracy of bounding boxes significantly, which offers advantages such as high scalability, low cost, and reduced training time. It consists of a global case feature learning module for extracting global feature map from the CT case and an object feature relation module for modeling the relationships between objects across slices. Experimental results demonstrate the effectiveness and efficiency of AOCN in correcting the output bounding boxes of several mainstream object detection networks, with a 6% to 14% improvement in Recall while requiring only a few training epochs.

Binary hashing is an effective approach for content-based image retrieval, and learning binary codes with neural networks has attracted increasing attention in recent years. However, the training of hashing neural networks is difficult due to the binary constraint on hash codes. In addition, neural networks are easily affected by input data with small perturbations. Therefore, a sensitive binary hashing autoencoder (SBHA) is proposed to handle these challenges by introducing stochastic sensitivity for image retrieval. SBHA extracts meaningful features from original inputs and maps them onto a binary space to obtain binary hash codes directly. Different from ordinary autoencoders, SBHA is trained by minimizing the reconstruction error, the stochastic sensitive error, and the binary constraint error simultaneously. SBHA reduces output sensitivity to unseen samples with small perturbations from training samples by minimizing the stochastic sensitive error, which helps to learn more robust features. Moreover, SBHA is trained with a binary constraint and outputs binary codes directly. To tackle the difficulty of optimization with the binary constraint, we train the SBHA with alternating optimization. Experimental results on three benchmark datasets show that SBHA is competitive and significantly outperforms state-of-the-art methods for binary hashing.

The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The treatment management of A A is highly dependent on the CT image diagnosis. However, the in-flamed appendix exhibits blurred boundaries with nearby tissue, varying shapes, and sizes. These properties require high robustness and generalization capability of inflamed appendix segmentation networks. In this paper, we propose a CNN-Transformer-based encoder-decoder segmentation network (LSSED) equipped with localized stochastic sensitivity (LSS) loss function and residual dilated paths (RD-Paths) to solve above problems. The proposed method effectively learns robust features of the input data by reducing the LSS of unseen samples. In addition, the RD-Paths capture multiscale feature information and reduce the semantic gap between the encoder and decoder, which improves the accuracy of the segmentation. Empirical studies on a real-world AA dataset show that our method yields the best performance in terms of average Dice similarity coefficient (DSC) and Hausdorff Distance of 95% (HD95) compared to several state-of-the-art segmentation networks.

Wood drying technology plays a key role in extending service lifetime of wood as the moisture content has a great influence on the wood quality. This paper presents a moisture content prediction model based on the deep long short-term memory (LSTM) autoencoders with stochastic sensitivity (DLASS) to extract a hidden representation of input data. The DLASS uses multiple LSTM encoders to learn more informative hidden representations from unseen samples, which are then decoded using multiple LSTM decoders. The DLASS is trained by minimizing the perturbed error from historical moisture content data. Furthermore, a nonlinear fully connected feedforward neural network as a regression layer is applied to predict moisture content using hidden representations learned by the DLASS. The DLASS is applied to real-world industrial data of Sugi wood processed by a drying kiln made by SECEA from August 4 to 19, 2008. Multiple test cases and comparisons with existing classical and state-of-the-art models show that the DLASS model yields more accurate moisture content prediction results and has high generalization capability. To be specific, the DLASS yields the lowest MAE (0.026), MAPE (0.280), and RMSE (0.058) for predicting moisture content during the wood drying process.

In this brief, we investigate the problem of incremental learning under data stream with emerging new classes (SENC). In the literature, existing approaches encounter the following problems: 1) yielding high false positive for the new class; i) having long prediction time; and 3) having access to true labels for all instances, which is unrealistic and unacceptable in real-life streaming tasks. Therefore, we propose the k -Nearest Neighbor ENSemble-based method (KNNENS) to handle these problems. The KNNENS is effective to detect the new class and maintains high classification performance for known classes. It is also efficient in terms of run time and does not require true labels of new class instances for model update, which is desired in real-life streaming classification tasks. Experimental results show that the KNNENS achieves the best performance on four benchmark datasets and three real-world data streams in terms of accuracy and F1-measure and has a relatively fast run time compared to four reference methods. Codes are available at https://github.com/Ntriver/KNNENS.

Undersampling is a popular method to solve imbalanced classification problems. However, sometimes it may remove too many majority samples which may lead to loss of informative samples. In this article, the hashing-based undersampling ensemble (HUE) is proposed to deal with this problem by constructing diversified training subspaces for undersampling. Samples in the majority class are divided into many subspaces by a hashing method. Each subspace corresponds to a training subset which consists of most of the samples from this subspace and a few samples from surrounding subspaces. These training subsets are used to train an ensemble of classification and regression tree classifiers with all minority class samples. The proposed method is tested on 25 UCI datasets against state-of-the-art methods. Experimental results show that the HUE outperforms other methods and yields good results on highly imbalanced datasets.

The Synthetic Minority Oversampling Technique (SMOTE) is effective to handle imbalance classification problems. However, the random candidate selection of SMOTE may lead to severe overlap between classes and introduce new noise factors. Many variants of SMOTE have been proposed to relieve these problems by generating new examples in safe regions. Most of these methods generate new examples with existing minority examples without considering the negative impact that class imbalance have brought on these examples. In this paper, we handle the imbalance classification using Bayes' decision rule and propose a novel oversampling method, the Minority Oversampling using Sensitivity (MOSS). Candidates for new example generations are selected considering their sensitivity with respect to class imbalance. New examples are then generated by interpolating the candidate and one of its adjacent examples. Experiments on 30 datasets confirm the superiority of the MOSS against one baseline method and seven oversampling methods.

In data streaming environments such as a smart grid, it is impossible to restrict each data chunk to have the same number of samples in each class. Hence, in addition to the concept drift, classification problems in streaming data environments are inherently imbalanced. However, streaming imbalanced and concept drifting problems in the power system and smart grid have rarely been studied. Incremental learning aims to learn the correct classification for the future unseen samples from the given streaming data. In this paper, we propose a new incremental ensemble learning method to handle both concept drift and class imbalance issues. The class imbalance issue is tackled by an imbalance-reversed bagging method that improves the true positive rate while maintains a low false positive rate. The adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers according to their classification performances and stochastic sensitivities. The proposed method is applied to a case study for the electricity pricing in Australia to predict whether the price of New South Wales will be higher or lower than that of Victorias in a 24-h period. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.

Current methods for nonintrusive load monitoring (NILM) problems assume that the number of appliances in the target location is known, however, this may not be realistic. In real-world situations, the initial setup of the site can be known but new appliances may be added by users after a period of time, especially in a household or nonrestrictive scenarios. In this sense, current methods without detecting new appliances may not accurately monitor loads of different appliances and scenarios. In this paper, a novel new appliance detection method is proposed for NILM with imbalance classification for appliances switching ON or OFF. The prediction of appliances being switched ON or OFF is an important step in load monitoring and the switching on frequencies for coffee machine and air conditioning in a household are different, making the problem inherently imbalanced. Experimental results show that the proposed method yields outstanding performance against the well-known oversampling method, synthetic minority oversampling technique, on real NILM applications in scenarios with new appliances emerging.

Class imbalance problems occur in many real-world applications. Oversampling methods are effective to handle class imbalance issues by replicating or generating new minority samples to rebalance the class distribution. However, current methods directly using all minority samples will also use noisy samples to generate new samples which may lead to more severe class overlapping and introduce more noisy samples. In this work, we propose a stochastic sensitivity measure-based noise filtering and oversampling method, i.e. the SSMNFOS, to improve the robustness of oversampling method with respect to noisy samples. Samples yielding high stochastic sensitivities are identified as noises by a neural network ensemble and will not participate in the oversampling method for rebalancing the class distribution. Comprehensive experimental studies are carried out on ten datasets with five different noise levels to analyze the effectiveness of the proposed method. Experimental results show that the SSMNFOS outperforms state-of-the-art methods with 95% statistical significance.

Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by both the stochastic sensitivity measure (SSM) and the AdaBoost algorithm to yield higher and more robust performances. Experimental results show that the BSMBoost yields better and more robust performances in comparison to other state-of-the-art boosting-based imbalanced classification methods.