Due to the inherent imbalance in real-world datasets, naïve Empirical Risk Minimization (ERM) tends to bias the learning process towards the majority classes, hindering generalization to minority classes. To rebalance the learning process, one straightforward yet effective approach is to modify the loss function via class-dependent terms, such as re-weighting and logit-adjustment. However, existing analysis of these loss-oriented methods remains coarse-grained and fragmented, failing to explain some empirical results. After reviewing prior work, we find that the properties used through their analysis are typically global, i.e., defined over the whole dataset. Hence, these properties fail to effectively capture how class-dependent terms influence the learning process. To bridge this gap, we turn to explore the localized versions of such properties i.e., defined within each class. Specifically, we employ localized calibration to provide consistency validation across a broader range of losses and localized Lipschitz continuity to provide a fine-grained generalization bound. In this way, we reach a unified perspective for improving and adjusting loss-oriented methods. Finally, a principled learning algorithm is developed based on these insights. Empirical results on both traditional ResNets and foundation models validate our theoretical analyses and demonstrate the effectiveness of the proposed method.

The automatic generation of medical imaging reports has significant research value, as it can alleviate the heavy workload of radiologists and reduce diagnostic biases. Existing methods mostly focus on text fluency, ensuring that generated reports align with ground truth. However, this leads to a neglect of the effective integration of clinical knowledge, resulting in poor clinical accuracy. To address this limitation, we propose a novel framework that incorporates clinical knowledge to improve clinical accuracy and report generation. Specifically, we introduce Multi-Expert Knowledge Enhanced Prompt Learning (KEP), which generates more reliable disease diagnostic prompts by integrating clinical knowledge, thereby enhancing clinical accuracy. Additionally, Knowledge-Enhanced Feature Learning (KEF) enhances image features with clinical knowledge, enabling the decoder to incorporate more comprehensive medical information. By integrating these knowledge-driven prompts and features into the generation model, our approach ensures clinical relevance and textual coherence. Experimental results demonstrate that our framework outperforms previous methods across multiple benchmarks, achieving superior clinical accuracy and linguistic fidelity.

Multi-modal retinal image registration is critical for clinical diagnosis. Conventional methods often neglect the reliability of keypoint matching, which may lead to registration errors that compromise clinical trustworthiness. To address this challenge, we propose a novel trusted retinal image registration method based on Evidential Deep Learning (EDL) with uncertain keypoint matching. First, the detector networks are employed to measure the confidence and uncertainty of each keypoint within the image pairs. Based on the measure, the top k confident keypoints are selected from corresponding images respectively. Second, the descriptor networks are used to extract the features of the keypoints and compute the similarity matrix for keypoint pairs. Crucially, we utilize Dempster-Shafer theory to fuse the uncertainties of keypoints into the similarity calculation, which facilitates prioritizing high-confident keypoint correspondences while suppressing unreliable matches. Finally, based on the similarity matrix with uncertainty measure, a PROSAC-based registration algorithm is implemented to achieve robust keypoint alignment. Experiments on the FIRE and PRIME-FP20 datasets demonstrate that the proposed method achieves state-of-the-art performance. Ablation studies further validate the effectiveness of uncertainty fusion in image registration, yielding a significant improvement of 13.8% in Mean Landmark Error AUC{@}6.25 compared to the previous best method. Besides enhancing accuracy, through measuring the uncertain keypoint matching, the proposed method can produce trusted retinal image registration results for clinicians.

Given a specific class space, open set recognition (OSR) requires a model to not only accurately classify data samples belonging to that class space, but also to identify samples from unseen classes. Based on prototype learning, existing OSR methods seek to build a separable feature space in which the unseen classes are as far away as possible from the given classes. However, these methods may overly focus on either the unseen classes or the given classes, resulting in unstable performance and lack of robustness. To handle this challenge, a robust OSR learning framework, named dual prototype learning is proposed. During the training phase, a set of dual prototypes is created for each given class, where one prototype (intra-class prototype) is used to form a compact intra-class feature representation, and the other prototype (anti-class prototype) is used to form a space that does not belong to the class by pushing the anti-class prototype away from the intra-class prototype. In the inference phase, our method can achieve a more stable and robust decision-making process by simultaneously measuring the distance between the sample and the intra-class prototypes, as well as the distance between the sample and the anti-class prototypes. Qualitative quantitative experiments demonstrate that the proposed method outperforms existing methods in terms of classification performance and robustness.

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classification process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. Comparing with the traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. Specifically, we construct an evidence extractor at each classification layer to extract multilayer (multi-grained) evidence for image classification. To fuse the extracted multi-grained evidence from coarse to fine, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into fine-grained classes to enhance the classification performances. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of fine-grained classification. The comparison with state-of-the-art FGIC methods shows that our proposed method achieves competitive performances.

Fine-Grained Medical Image Classification (FGMIC) aims to identify disease subclasses within the corresponding metaclass. Due to insufficient labeled images and confusing image samples, the accuracy of FGMIC is limited and the trustworthiness of the model is also affected. In this paper, we utilize evidence theory to measure prediction uncertainty and improve the trustworthiness of FGMIC through multiple evidence fusion. Specifically, we consider FGMIC as a hierarchical classification process. At each layer, we construct an evidential classifier to extract classification evidence. Evidence extracted from all layers forms multi-grained evidence. Then, multi-grained evidence are fused through the Dirichlet hyper-PDF, so that evidence of coarse-grained layer classes can be used to enhance the corresponding evidence of fine-grained layer classes. Moreover, the scanned 3D medical image of a patient can generally be divided into three 2D views, with different views containing different features and uncertainties of the pathological region. Inspired by this, the evidential classifier of each layer is split into three sub-evidential classifiers, where one sub-evidential classifier is built on a view. Then, classification evidence from different views is fused using uncertainty-weighted fusion. Experiments on two cancer subtype classification tasks validate that multiple evidence fusion can not only improve prediction accuracy, but also reduce uncertainty and improve the trustworthiness.

Object Detection (OD) can be viewed as a multi-objective task to achieve object localization and class recognition. With the rapid development of the deep neural networks (DNNs), on the one hand, the performance of OD has been significantly improved by relying on the high-quality feature extraction and representation of DNNs. On the other hand, it can be challenging to accurately detect and recognize objects with non-salient or confusing features. In this paper, we propose an efficient and pluggable OD method by using attention mechanism to solve these issues from two aspects. Firstly, we exploit the semantic relationship between objects as a prior knowledge to reduce the incorrect recognition of objects with confusing features, where the relationship is encoded as an attention map by using a graph convolutional network, and then this attention map is used to reweight the feature intensities of objects belonging to different classes. Then, based on the feature map extracted from DNNs, we extract a sub-feature map containing foreground information and use this map to generate foreground attention map to improve the feature saliency of the objects. The qualitative and quantitative experimental results well verify the effectiveness of our method.

In recent years, significant progress has been made in 3D object detection. The focus of research has primarily been on improving the detection accuracy of models, however, neglecting their efficiency during actual deployment. Aiming at this issue, in this paper, we propose a multi-dimensional pruning method from the perspectives of data and model. Specifically, given the input data represented by the voxel grid, we first measure the voxel importance and propose an importance-based sampling module to sparsify voxels while preserving informative ones. The model pruning is wrapped in the framework of weighted voxel distillation, where the student model is obtained by pruning the channels of teacher model and only the informative voxels in the teacher model are involved and transferred to the students. In addition, the proposed method can be seamlessly integrated into current voxel-based 3D detectors without any additional costs. Experimental results on the KITTI and ONCE datasets show that our method can achieve a reduction of over 80% in GFLOPs while maintaining superior performance.

Medical insurance plays a vital role in modern society, yet organized healthcare fraud causes billions of dollars in annual losses, severely harming the sustainability of the social welfare system. Existing works mostly focus on detecting individual fraud entities or claims, ignoring hidden conspiracy patterns. Hence, they face severe challenges in tackling organized fraud. In this paper, we proposed RDPGL, a novel Risk Diffusion-based Parallel Graph Learning approach, to fighting against medical insurance criminal gangs. In particular, we first leverage a heterogeneous graph attention network to encode the local context from the beneficiary-provider graph. Then, we devise a community-aware risk diffusion model to infer the global context of organized fraud behaviors with the claim-claim relation graph. The local and global representations are parallel concatenated together and trained simultaneously in an end-to-end manner. Our approach is extensively evaluated on a real-world medical insurance dataset. The experimental results demonstrate the superiority of our proposed approach, which could detect more organized fraud claims with relatively high precision compared with state-of-the-art baselines.

Pre-training deep learning models on ImageNet and transferring the models to medical image applications facilitate to improve the medical image analysis and reduce the need for labeled medical image data. However, some images from ImageNet may be fundamentally different from medical images in feature representation and lead to the negative transfer effects. To deal with this issue, we propose a novel strategy based on evidence theory to select reliable instances from ImageNet for medical image domain adaptation. Specifically, we formulate an evidential mass function to measure the ignorance and reliability of the images from ImageNet with respect to the classification tasks of medical images. Through selecting reliable instances with low ignorance degree from ImageNet, we can enhance the transfer performances of deep neural networks in medical image domain adaptation. Moreover, the proposed data selection strategy is independent of specific learning algorithm and can be viewed as a common preprocessing technique. Numerical experiments on tomography images, X-Ray images, and ultrasound images are given to comprehensively demonstrate the effectiveness of the selection strategy.

Bladder cancer staging is crucial for operation planning and cancer assessment. Deep Convolutional Neural Networks (DCNNs) have been widely used to classify the bladder tumor images to identify cancer stages. However, the pure image-based deep learning methods over depend on the labeled data training and neglect the clinical priors. Human doctors judge the stage of a bladder tumor through checking whether the tumor infiltrating into bladder wall. The clinical priors of tumor infiltration are helpful to improve the DCNN-based bladder cancer staging and make the predictions coincide with the law of medicine. To involve clinical priors into deep learning for cancer staging, we propose a DCNN model with prior evidence to classify medical images of bladder tumors. Specifically, we measure the degree of tumor infiltrating into bladder wall to construct the prior evidence and integrate the prior evidence into the image-based prediction with evidential deep neural networks. We analyze the learning objective and prove that the prior evidences consistent with the ground truth will certainly reduce the prediction error and variance produced by image-based neural networks. The experiments on bladder cancer MR images datasets validate that involving prior evidences is effective to improve the DCNN-based cancer staging.

Segmenting bladder tumors from MR images is important for the early detection and auxiliary diagnosis of bladder cancer. In recent years, Deep Convolutional Neural Networks (DCNNs) have been widely used for bladder tumor segmentation. However, the DCNN-based tumor segmentation methods generally over-depend on the model training with labeled data, and neglect the priors that can be learned from data or domain experts. The priors of tumor size and location are very helpful to improve the segmentation precision of bladder tumors, especially for the situation when the labeled images are insufficient to cover the diversity. To tackle the problem, we proposed a method to integrate priors into DCNN-based bladder tumor segmentation in this paper. The priors consist of both specific prior from data and general prior from domain knowledge. Specifically, we add a decoding path into the segmentation network to generate the specific prior, and formulate the general prior of domain knowledge to guide the generation of specific prior. Comprehensive experiments on massive bladder MR images clearly validate the effectiveness of the proposed bladder tumor segmentation method with priors.

Topic model and word embedding reflect two perspectives of text semantics. Topic model maps documents into topic distribution space by utilizing word collocation patterns within and across documents, while word embedding represents words within a continuous embedding space by exploiting the local word collocation patterns in context windows. Clearly, these two types of patterns are complementary. In this paper, we propose a novel integration framework to combine the two representation methods, where topic information can be transmitted into corresponding semantic embedding structure. Based on this framework, we construct a Embedding Enhanced Topic Model (EETM), which can improve topic modeling and generate topic embeddings by leveraging the word embedding. Extensive experimental results show that EETM can learn high-quality document representations for common text analysis tasks across multiple data sets, indicating it is very effective for merging topic models with word embeddings.

Wireless sensor networks (WSN) have been in widely use due to its prominent capability of data sensing and processing. However, the lifetime of WSN is constrained because of the limited battery energy in each sensor node. Fortunately, recent breakthrough in the area of wireless energy transfer technology has created a new dimension to prolonging the lifetime of WSN. In this paper, we firstly introduce the conception and requirements of cyclic energy conservation in conditions of rechargeable WSN via Wireless Charging Vehicle (WCV). Then, in order to enhance the utility of charging, optimization of the minimal occupied time of WCV achieved by Cuckoo Search is carried out, hence leading to the prior solution of TSP and essential properties of cyclic energy conservation. Furthermore, the optimal solution of cyclic energy conservation in WSN is presented and the simulation result of the proposed scheme is given. Lastly, a conclusion of our research is drawn and a potential outlook of research is given as well.