Accurate histopathological image segmentation is crucial for precise disease diagnosis and prognosis. Yet, challenges like staining variations, imaging conditions, and tissue diversity impede model generalization across domains, such as different institutes or organs. Traditional domain generalization (DG) techniques, such as data augmentation and feature alignment, excel in classification tasks but face challenges in segmentation tasks due to their dense prediction requirements. These tasks are particularly computationally demanding, and are complicated due to the fine-grained feature variability that arises from the domain differences in histopathological images. To tackle this, we propose the Shallow-Deep Synergy (SDS) approach for the U-Net-based segmentation framework, which capitalizes on the distinctive characteristics of both shallow and deep layers of the U-Net. Specifically, we introduce the fine-grained domain variations in image intensities and textures for shallow layers, while focusing on aligning the pixel-level classification decision boundaries in deep layers by adjusting the optimization trajectory through class-wise gradient and feature alignment. Moreover, the SDS is equipped with a big-batch strategy further boosting alignment efficiency, achieving high accuracy without substantial GPU memory. Extensive experiments conducted on two histopathological segmentation datasets, each representing different domain types, demonstrate that the proposed SDS achieves superior generalization performance compared to existing domain generalization methods, even being competitive with intra-domain models in some cases.

Aiming at the shortcomings of the traditional Density-Based Spatial Clustering of Applications with Noise -DBSCAN algorithm such as insignificant clustering effect and the choice of parameter combinations. This paper proposes an AD-DBSCAN algorithm with adaptive parameters, which makes the algorithm more difficult in the selection of the parameters. By establishing a DBSCAN algorithm model to adapt to finding the optimal distance threshold and the minimum number of neighbor points, the clustering is more accurate, and the noise point identified in the data is more accurate. Through the observation of the calculation model of the Calinski-Harabasz index, the evaluation index of the clustering algorithm, the selection of the optimal best distance threshold and the minimum number of neighborhood points, the accuracy of noise point recognition is improved by 5 times in the clustering algorithm, and the Calinski-Harabasz index improved by about 39.84%. The applicability of the algorithm in clustering the locations of urban road traffic accidents is verified.

Modern internet platforms usually have different scenarios to provide rich recommendation services to meet the diverse demands of users. Cross-domain recommendation (CDR) and multi-domain recommendation (MDR) methods are widely used in such platforms to leverage rich auxiliary information from multiple domains. However, state-of-the-art CDR and MDR methods usually enforce some correlations between source and target embeddings on each user, ignoring the correlations between users in both domains. To address this problem, we adopt a relaxed contrastive loss, that employs the pairwise similarities in the source domain as relaxed labels, enforcing such inter-sample relations are reserved in a weighted manner in the target domain. The basic assumption behind such a design is that users with similar interests should be with similar interacted items in a rec- ommender system, and this work takes a step further to realize and specify such similarity modeling as collaborative signals encoded in both implicit embedding spaces. We validate the effectiveness of the proposed method on a large- scale public dataset and a real production dataset with over 700 million samples. We further experimentally show that the proposed embedding transfer method is generic, and can be plugged into any existing deep neural networks, such as YoutubeDNN and BERT4Rec. Currently, the proposed embedding transfer techniques have been successfully deployed in the Guess You Like in WeTV for the CDR/MDR task.

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at:https://github.com/RoyalSkye/AGH.

Thanks to the huge accumulation of Electronic Health Records (EHRs), numerous deep learning based predictive models were proposed for this task. Among them, most of the existing state-of-the-art (SOTA) models were built with recurrent neural networks (RNNs). Regardless of their success, RNN-based models mainly suffer from three limitations. (i) Accuracy: the prediction accuracy of RNN-based models drops quickly as the length of EHR sequences increases. (ii) Efficiency: the recurrence property of RNN-based models makes the computation parallelization impossible, and accordingly hurts the efficiency of such models in practice. (iii) Interpretability: the outputs of RNN-based models are difficult to explain due to the unexplainable nature of deep models. In this paper, we resort to the recently advanced attention mechanism to model the dependencies between inputs and outputs, which overcomes shortages of RNN-based models in accuracy and efficiency. As for interpretability, we model the relationships with two linear mappings from the input to the output, which account for two important factors—one is for context-aware information and the other is for time-aware representation—of capturing discriminative features in learning patient’s representations. We empirically demonstrate the effectiveness of the proposed model in both accuracy and computational efficiency, meanwhile, analyze and discuss the reasonability of each explanation approach.

Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets with expert annotations at the cell level for good results and typically suffer from poor generalization. Secondly, they simply treat the BM cytomorphological examination as a multi-class cell classification task, thus failing to exploit the correlation among leukemia subtypes over different hierarchies. Therefore, BM cytomorphological estimation as a time-consuming and repetitive process still needs to be done manually by experienced cytologists. Recently, Multi-Instance Learning (MIL) has achieved much progress in data-efficient medical image processing, which only requires patient-level labels (which can be extracted from the clinical reports). In this paper, we propose a hierarchical MIL framework and equip it with Information Bottleneck (IB) to tackle the above limitations. First, to handle the patient-level label, our hierarchical MIL framework uses attention-based learning to identify cells with high diagnostic values for leukemia classification in different hierarchies. Then, following the information bottleneck principle, we propose a hierarchical IB to constrain and refine the representations of different hierarchies for better accuracy and generalization. By applying our framework to a large-scale childhood acute leukemia dataset with corresponding BM smear images and clinical reports, we show that it can identify diagnostic-related cells without the need for cell-level annotations and outperforms other comparison methods. Furthermore, the evaluation conducted on an independent test cohort demonstrates the high generalizability of our framework.

Pathological primary tumor (pT) stage focuses on the infiltration degree of the primary tumor to surrounding tissues, which relates to the prognosis and treatment choices. The pT staging relies on the field-of-views from multiple magnifications in the gigapixel images, which makes pixel-level annotation difficult. Therefore, this task is usually formulated as a weakly supervised whole slide image (WSI) classification task with the slide-level label. Existing weakly-supervised classification methods mainly follow the multiple instance learning paradigm, which takes the patches from single magnification as the instances and extracts their morphological features independently. However, they cannot progressively represent the contextual information from multiple magnifications, which is critical for pT staging. Therefore, we propose a structure-aware hierarchical graph-based multi-instance learning framework (SGMF) inspired by the diagnostic process of pathologists. Specifically, a novel graph-based instance organization method is proposed, namely structure-aware hierarchical graph (SAHG), to represent the WSI. Based on that, we design a novel hierarchical attention-based graph representation (HAGR) network to capture the critical patterns for pT staging by learning cross-scale spatial features. Finally, the top nodes of SAHG are aggregated by a global attention layer for bag-level representation. Extensive studies on three large-scale multi-center pT staging datasets with two different cancer types demonstrate the effectiveness of SGMF, which outperforms state-of-the-art up to 5.6% in the F1 score.

Predicting drug combinations according to patients' electronic health records is an essential task in intelligent healthcare systems, which can assist clinicians in ordering safe and effective prescriptions. However, existing work either missed/underutilized the important information lying in the drug molecule structure in drug encoding or has insufficient control over Drug-Drug Interactions (DDIs) rates within the predictions. To address these limitations, we propose CSEDrug, which enhances the drug encoding and DDIs controlling by leveraging multi-faceted drug knowledge, including molecule structures of drugs, Synergistic DDIs (SDDIs), and Antagonistic DDIs (ADDIs). We integrate these types of knowledge into CSEDrug by a graph-based drug encoder and multiple loss functions, including a novel triplet learning loss and a comprehensive DDI controllable loss. We evaluate the performance of CSEDrug in terms of accuracy, effectiveness, and safety on the public MIMIC-III dataset. The experimental results demonstrate that CSEDrug outperforms several state-of-the-art methods and achieves a 2.93% and a 2.77% increase in the Jaccard similarity scores and F1 scores, meanwhile, a 0.68% reduction of the ADDI rate (safer drug combinations), and 0.69% improvement of the SDDI rate (more effective drug combinations).

Distance metric learning has become a popular solution for few-shot Named Entity Recognition (NER). The typical setup aims to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. The effect of this setup may, however, be compromised for two reasons. First, there is typically a limited optimization exerted on the representations of entity tokens after initing by pre-trained language models. Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting. To address these challenges, we propose a novel approach named COntrastive learning with Prompt guiding for few-shot NER (COPNER). We introduce a novel prompt composed of class-specific words to COPNER to serve as 1) supervision signals for conducting contrastive learning to optimize token representations; 2) metric referents for distance-metric inference on test samples. Experimental results demonstrate that COPNER outperforms state-of-the-art models with a significant margin in most cases. Moreover, COPNER shows great potential in the zero-shot setting.

Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relationship between nodes as either hard positive pairs or hard negative pairs. This leads to the loss of structural information, and lacks the mechanism to generate positive pairs for nodes with few neighbors. To overcome limitations, we propose a novel soft link-based sampling method, namely MixDec Sampling, which consists of Mixup Sampling module and Decay Sampling module. The Mixup Sampling augments node features by synthesizing new nodes and soft links, which provides sufficient number of samples for nodes with few neighbors. The Decay Sampling strengthens the digestion of graph structure information by generating soft links for node embedding learning. To the best of our knowledge, we are the first to model sampling relationships between nodes by soft links in GNN-based recommender systems. Extensive experiments demonstrate that the proposed MixDec Sampling can significantly and consistently improve the recommendation performance of several representative GNN-based models on various recommendation benchmarks.

Patient similarity measurement is an essential step in discovering clinically meaningful subgroups and building case retrieval systems. Most existing studies implement this procedure using similarity measurement algorithms on the multivariate clinical time-series (input space) or the low-dimensional patient representation (representation space) learned by a representation learning model. However, they either suffer from the adverse effects of irrelevant variables in the data or fail to assess the fine-grained similarity underneath the disease progress. In this paper, we propose a method to measure more fine-grained patient similarity in the state space, where each patient is represented by a series of state representations that reveal the dynamic health status. We discuss three desiderata, including stability, personality, and interpretability, for the state representations, and on this basis, develop a supervised predictive model that learns good state representations for identifying similar patients and predicting patient outcomes. Experimental results on the publicly available dataset MIMIC-III show that our method offers a promising direction for precisely identifying similar patients at the state trajectory level, as well as accurately predicting outcomes.

Tissue segmentation is an essential task in computational pathology. However, relevant datasets for such a pixel-level classification task are hard to obtain due to the difficulty of annotation, bringing obstacles for training a deep learning-based segmentation model. Recently, contrastive learning has provided a feasible solution for mitigating the heavy reliance of deep learning models on annotation. Nevertheless, applying contrastive loss to the most abstract image representations, existing contrastive learning frameworks focus on global features, therefore, are less capable of encoding finer-grained features (e.g., pixel-level discrimination) for the tissue segmentation task. Enlightened by domain knowledge, we design three contrastive learning tasks with multi-granularity views (from global to local) for encoding necessary features into representations without accessing annotations. Specifically, we construct: (1) an image-level task to capture the difference between tissue components, i.e., encoding the component discrimination; (2) a superpixel-level task to learn discriminative representations of local regions with different tissue components, i.e., encoding the prototype discrimination; (3) a pixel-level task to encourage similar representations of different tissue components within a local region, i.e., encoding the spatial smoothness. Through our global-to-local pre-training strategy, the learned representations can reasonably capture the domain-specific and fine-grained patterns, making them easily transferable to various tissue segmentation tasks in histopathological images. We conduct extensive experiments on two tissue segmentation datasets, while considering two real-world scenarios with limited or sparse annotations. The experimental results demonstrate that our framework is superior to existing contrastive learning methods and can be easily combined with weakly supervised and semi-supervised segmentation methods.

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify ‘truly similar patients’ (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors’ importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.

Deep learning has proved to be a powerful tool for medical image analysis in recent years. Data imbalance is a common problem in medical images. Dice Loss is widely used in medical image segmentation tasks to address the data imbalance problem. However, it only addresses the imbalance problem between foreground and background yet overlooks another imbalance between easy and hard examples that also severely affects the training process of a learning model. Empirically speaking, an easy example generally contributes less to the overall loss than a hard example. However, in practice, compared with hard examples, a large number of easy examples will be generated from a medical image and will dominate the training model, resulting in sub-optimal training or worse. To tackle this problem, we propose a novel Focal Dice Loss to alleviate the imbalance between hard examples and easy examples. Focal Dice Loss is able to reduce the contribution from easy examples and make the model focus on hard examples through our proposed novel balanced sampling strategy during the training process. Furthermore, to evaluate the effectiveness of our proposed loss functions, we conduct extensive experiments on two real-world medical image datasets with 2D and 3D convolutional neural networks. The experimental results show that our proposed Focal Dice Loss brings a significant improvement in segmentation performance compared to Dice Loss. Moreover, we find that our proposed Focal Dice Loss can effectively alleviate the over-fitting problem.

Building a predictive model based on historical Electronic Health Records (EHRs) for personalized healthcare has become an active research area. Benefiting from the powerful ability of feature extraction, deep learning (DL) approaches have achieved promising performance in many clinical prediction tasks. However, due to the lack of interpretability and trustworthiness, it is difficult to apply DL in real clinical cases of decision making. To address this, in this paper, we propose an interpretable and trustworthy predictive model~(INPREM) for healthcare. Firstly, INPREM is designed as a linear model for interpretability while encoding non-linear relationships into the learning weights for modeling the dependencies between and within each visit. This enables us to obtain the contribution matrix of the input variables, which is served as the evidence of the prediction result(s), and help physicians understand why the model gives such a prediction, thereby making the model more interpretable. Secondly, for trustworthiness, we place a random gate (which follows a Bernoulli distribution to turn on or off) over each weight of the model, as well as an additional branch to estimate data noises. With the help of the Monto Carlo sampling and an objective function accounting for data noises, the model can capture the uncertainty of each prediction. The captured uncertainty, in turn, allows physicians to know how confident the model is, thus making the model more trustworthy. We empirically demonstrate that the proposed INPREM outperforms existing approaches with a significant margin. A case study is also presented to show how the contribution matrix and the captured uncertainty are used to assist physicians in making robust decisions.

Predicting the future health conditions of patients based on Electronic Health Records (EHR) is an important research topic. Due to the temporal nature of EHR data, the major challenge is how to properly model the sequences of patient visits. Recurrent Neural Networks (RNNs) with attention mechanisms are widely employed to address this challenge, but often vulnerable to data insufficiency. Lately, predictive models with the guidance of medical knowledge have been proposed to solve this problem and achieve superior performance. Although these models learn reasonable embeddings (infused with knowledge) for clinical variables, they are not able to fully make use of the underlying information in the knowledge graph. To address this, we propose an end-to-end robust solution, namely Graph Neural networks based Diagnosis Prediction (GNDP), to predict future conditions for patients. Compared with existing methods, GNDP learns the spatial and temporal patterns from patients' sequential graph, in which the domain knowledge is naturally infused. We evaluate our GNDP model against a set of state-of-the-art methods on two real-world EHR datasets and the results demonstrate that our approach significantly outperforms the baseline methods.

Hand-crafted features have been proven useful in solving the electrocardiograph~(ECG) classification problem. The features rely on domain knowledge and carry clinical meanings. However, the construction of the features requires tedious fine tuning in practice. Lately, a set of end-to-end deep neural network models have been proposed and show promising results in ECG classification. Though effective, such models learn patterns which usually mismatch human's concept, and thereby it is hard to get a convincing explanation with interpretation methods. This limitation significantly narrows the applicability of deep models, considering it is difficult for cardiologists to accept the unexplainable results from deep learning. To alleviate such limitation, we are bringing the best from the two worlds and propose a domain knowledge guided deep neural network. Specifically, we utilize a deep residual network as a classification framework, within which key feature ~(P-wave and R-peak position) reconstruction tasks are adopted to incorporate domain knowledge in the learning process. The reconstruction tasks make the model pay more attention to key feature points within ECG. Furthermore, we utilize occlusion method to get visual interpretation and design a visualization at both heartbeat level and feature point level. Our experiments show the superior performance of the proposed ECG classification methods compared to the model without P-wave and R-peak tasks, and the patterns learnt by our model is more explainable.

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.

Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of data-driven clinical risk prediction approaches have been spawned in recent years. However, there remain three challenges, which if addressed would improve the performance and applicability of such models. (i) Due to the limited data sharing between different health care institutions, the EHR data collected by a single institution is often inadequate or missing some visits records. The limited number of data cannot meet the large sample required of recent approaches especially deep learning models. In addition, the missing records (due to visiting different institution) may contain important health condition of the patient, which if ignored would cause prediction bias. (ii) Few existing approaches take clinical knowledge into account. The auxiliary knowledge if included can greatly reduce the data dependency of many modern learning algorithms. (iii) Most existing deep learning based methods are unable to identify the contribution of each medical event to the final results, which prohibits such models from being widely accepted in practical clinical applications. In this paper, we propose an interpretable and knowledge-guided deep model to address these challenges. Specifically, we distill knowledge from a clinical knowledge graph both explicitly and implicitly, which can not only supplement inadequate patient records but also guide the predicting process of the model. Furthermore, skip-connections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several state-of-the-art methods. Finally, a series of case studies are presented to prove the interpretability of our model.

Lately, thanks to the huge amount of Electronic Health Records (EHR) data, deep learning models have been successfully applied to a variety of clinical prediction problems. Existing state-of-the-art clinical predicting models are usually built with recurrent neural network (RNN) and attention mechanism. However, such RNN based approaches mainly suffer from three limitations on clinical predictions, which if addressed would significantly widen their applicability. (i) Accuracy: The performance of RNN based models drops fast when the length of EHR sequences increases. (ii) Interpretability: The prediction results of RNN based models are hard to interpret due to the nature of deep models. (iii) Efficiency: The sequential property of RNN based models makes the parallelization of computation impossible, and accordingly hurts the efficiency of such models in practice. In this paper, we propose an efficient attention-based model to address the above three challenges simultaneously. In the context of heart failure prediction task, we demonstrate the interpretation capability of our model by visualizing relative connections between events and prediction result, and the high computational efficiency comparing to other baseline methods. Meanwhile, we show that the accuracy of our prediction model is comparable or better than those of other state-of-the-art prediction models in healthcare applications.