Publications by authors named "Hong-Yu Zhou"

In many clinical and research settings, the scarcity of high-quality medical imaging datasets has hampered the potential of artificial intelligence (AI) clinical applications. This issue is particularly pronounced in less common conditions, underrepresented populations and emerging imaging modalities, where the availability of diverse and comprehensive datasets is often inadequate. To address this challenge, we introduce a unified medical image-text generative model called MINIM that is capable of synthesizing medical images of various organs across various imaging modalities based on textual instructions.

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Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer.

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Article Synopsis
  • The digital twin (DT) is a digital replica of a physical object or system that provides real-time updates and predictions about the physical counterpart's functionality.
  • In healthcare, DTs have the potential to transform patient diagnosis and treatment, but face challenges like technical issues, biological variability, and ethical concerns.
  • The text discusses the foundational concepts of DTs, their implementation in medicine, current healthcare applications, and outlines five key features necessary for advancing healthcare DT systems.
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Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges.

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Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.

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Background: Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exhibits multi-organ damage with diverse complications, the correlation between age, gender, medical history and clinical manifestations of novel coronavirus disease 2019 (COVID-19) patients was investigated.

Methods: 1640 patients who were infected with SARS-CoV-2 and hospitalized at the First Affiliated Hospital of Ningbo University from 22 December 2022 to 1 March 2023 were categorized and analysed. Normal distribution test and variance homogeneity test were performed.

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Pancreatic ductal adenocarcinoma (PDAC) is extremely malignant with limited treatment options. Deubiquitinases (DUBs), which cleave ubiquitin on substrates, can regulate tumor progression and are appealing therapeutic targets, but there are few related studies in PDAC. In our study, we screened the expression levels and prognostic value of USP family members based on published databases and selected USP10 as the potential interventional target in PDAC.

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Article Synopsis
  • The study investigates the role of antinuclear antibodies (ANAs) in the long-term prognosis of Chinese patients with Neuromyelitis optica spectrum disorder (NMOSD), revealing potentially significant correlations.
  • Among 525 patients, those with ANA showed higher frequencies of certain antibodies, higher disability scores, and more severe disease attacks compared to those without ANAs.
  • The findings suggest that ANA presence might indicate more severe disease activity and disability in NMOSD patients, though there was no significant difference in the annual recurrence rate between the two groups.
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Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to learn more contextualized visual representations. However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations.

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During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner.

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Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g.

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Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRD, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRD comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution.

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Virtual reality therapy (VRT) is a new psychotherapeutic approach integrating virtual reality technology and psychotherapy. This case series aimed to study effectiveness of VRT in treating psychological problems. We described four cases of first-line health care professionals with emerging clinically significant early psychological problems during the COVID-19 outbreak, and specifically received the VRT treatment.

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Background: Many patients with neurological disorders experience chronic fatigue, but the neural mechanisms involved are unclear.

Objective: Here we investigated whether the brain structural and functional connectivity alterations were involved in fatigue related to neuromyelitis optica spectrum disorder (NMOSD).

Methods: This prospective pilot study used structural and resting-state functional brain magnetic resonance imaging to compare total cortical thickness, cortical surface area, deep gray matter volume and functional connectivity (FC) between 33 patients with NMOSD and 20 healthy controls (HCs).

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Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. However, traditional semi-supervised segmentation methods fail to exploit unpaired multi-modal data, which are widely adopted in today's clinical routine.

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Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.

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Article Synopsis
  • This study explores how the molecular structure of contaminants affects their degradation efficiency and the types of reactive oxygen species (ROS) produced during advanced oxidation processes (AOPs) using a peroxymonosulfate (PMS) activation system.
  • It was found that the degradation of 19 different contaminants varied significantly, with specific ROS like sulfate (SO), hydroxyl (OH), and superoxide (O) dominating depending on the contaminant type, influenced by their structural characteristics.
  • The research suggests that understanding these molecular interactions can help design more effective AOPs for wastewater treatment, making them more practical and efficient in real-world applications.
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An enriched environment is used as a behavioral intervention therapy that applies sensory, motor, and social stimulation, and has been used in basic and clinical research of various neurological diseases. In this study, we established mouse models of photothrombotic stroke and, 24 hours later, raised them in a standard, enriched, or isolated environment for 4 weeks. Compared with the mice raised in a standard environment, the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated.

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  • Ocular myasthenia gravis (OMG) frequently progresses to generalized myasthenia gravis within two years of symptom onset, prompting this study to identify risk factors in patients not receiving immunosuppression or thymectomy treatment.
  • The researchers analyzed clinical data from 572 OMG patients across seven hospitals in China, focusing on various characteristics including age, symptoms, and antibody presence to determine factors influencing generalization of the disease.
  • Findings indicated that early and late onset in adulthood, abnormal repetitive nerve stimulation results, positive anti-acetylcholine receptor antibodies, and the presence of thymoma significantly increased the risk of disease generalization among these patients.
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Objective: To explore the efficacy and mechanism of using 3-n-butylphthalide (NBP) in combination with bone marrow mesenchymal stem cells (BMSCs) in the treatment of experimental autoimmune encephalomyelitis (EAE) in mice.

Methods: Myelin oligodendrocyte glycoprotein (MOG35-55) was used for the induction and establishment of the EAE model in C57BL/6 mice. The mice were randomly assigned to the EAE group, which received intraperitoneal injection of phosphate-buffered saline (PBS), the NBP-treated EAE group, or the NBP group, which received intraperitoneal injection of NBP, the BMSCs transplantion EAE group, or the BMSCs group, which received BMSCs injected into the lateral ventricle and intraperitoneal injection of PBS, and the BMSCs and NBP combination treatment EAE group, or the BMSCs+NBP group, which received BMSCs injected into the lateral ventricle and intraperitoneal injection of NBP.

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Background: Awake craniotomy has been widely used for tumor resection, epilepsy surgery, deep brain stimulation, and carotid endarterectomy. The report on awake artery malformation clipping is rare, especially for anesthesia management.

Case Summary: A 62-year-old female diagnosed with malformation of anterior cerebral artery at the right side.

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Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentional region generation modules. In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects.

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Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis.

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Scoliosis is a common medical condition, which occurs most often during the growth spurt just before puberty. Untreated Scoliosis may cause long-term sequelae. Therefore, accurate automated quantitative estimation of spinal curvature is an important task for the clinical evaluation and treatment planning of Scoliosis.

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