Jinyu Cong

Guoli Wang, Pingping Wang, Jinyu Cong et al.

Multi-label chest X-ray (CXR) recognition involves simultaneously diagnosing and identifying multiple labels for different pathologies. Since pathological labels have rich information about their relationship to each other, modeling the co-occurrence dependencies between pathological labels is essential to improve recognition performance. However, previous methods rely on state variable coding and attention mechanisms-oriented to model local label information, and lack learning of global co-occurrence relationships between labels. Furthermore, these methods roughly integrate image features and label embedding, ignoring the alignment and compactness problems in cross-modal vector fusion.To solve these problems, a Bi-modal Bridged Graph Convolutional Network (BB-GCN) model is proposed. This model mainly consists of a backbone module, a pathology Label Co-occurrence relationship Embedding (LCE) module, and a Transformer Bridge Graph (TBG) module. Specifically, the backbone module obtains image visual feature representation. The LCE module utilizes a graph to model the global co-occurrence relationship between multiple labels and employs graph convolutional networks for learning inference. The TBG module bridges the cross-modal vectors more compactly and efficiently through the GroupSum method.We have evaluated the effectiveness of the proposed BB-GCN in two large-scale CXR datasets (ChestX-Ray14 and CheXpert). Our model achieved state-of-the-art performance: the mean AUC scores for the 14 pathologies were 0.835 and 0.813, respectively.The proposed LCE and TBG modules can jointly effectively improve the recognition performance of BB-GCN. Our model also achieves satisfactory results in multi-label chest X-ray recognition and exhibits highly competitive generalization performance.

Bin Wang, Fan Wang, Pingping Wang et al.

In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction. Existing unlearning methods target parameters alone, leaving two critical gaps: (i) parameter-memory backflow, where retrieval reactivates parametric remnants or memory artifacts reintroduce sensitive content, and (ii) the absence of a unified strategy that covers both parameter and memory pathways. We present Synchronized Backflow Unlearning (SBU), a framework that unlearns jointly across parameter and memory pathways. The memory pathway performs dependency closure-based unlearning that prunes isolated entities while logically invalidating shared artifacts. The parameter pathway employs stochastic reference alignment to guide model outputs toward a high-entropy prior. These pathways are integrated via a synchronized dual-update protocol, forming a closed-loop mechanism where memory unlearning and parametric suppression reinforce each other to prevent cross-pathway recontamination. Experiments on medical QA benchmarks show that SBU reduces traces of targeted private information across both pathways with limited degradation on retained data.

Tianyang Li, Zhongyi Han, Benzheng Wei et al.

This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive. While a few pioneering works have made much progress, they underestimate both crucial bottlenecks. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained classification and cost-sensitive learning. Firstly, DCSL develops a conditional center loss that learns deep discriminative representation. Secondly, DCSL establishes score-level cost-sensitive learning that can adaptively enlarge the cost of misclassifying COVID-19 examples into other classes. DCSL is so flexible that it can apply in any deep neural network. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. Extensive experiments on the three-class classification show that our algorithm remarkably outperforms state-of-the-art algorithms. It achieves an accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an F1-score of 96.98%. These results endow our algorithm as an efficient tool for the fast large-scale screening of COVID-19.