期刊信息

International Journal of Biomedical Imaging (IJBI)

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影响因子:
1.3
出版商:
Hindawi
ISSN:
1687-4188
浏览:
21581
关注:
3

征稿

International Journal of Biomedical Imaging (IJBI) is an academic journal published by Hindawi. (ISSN 1687-4188, impact factor 1.3).

Aims and scope The last quarter century has witnessed major advancements that have brought biomedical imaging to a paramount status in life sciences. As a prominent example, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) was established in 2000 as the newest institute in the National Institutes of Health (NIH), USA. Generally speaking, the scope of biomedical imaging covers data acquisition, image reconstruction, and image analysis, involving theories, methods, systems, and applications. While tomographic and post-processing techniques become increasingly sophisticated, traditional and emerging modalities play more and more critical roles in anatomical, functional, cellular, and molecular imaging. The overall goal of the International Journal of Biomedical Imaging is to promote research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. International Journal of Biomedical Imaging is operated by a board of editors consisting of internationally known active researchers. The journal is made available online for free and can also be purchased in print. It utilizes a web-based review process for speedy turnaround up to high standards. In addition to regular issues, special issues will be organized by guest editors. Subject areas include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
由 Dou Sun 最后更新于

Special Issues

Special Issue on Explainable and Trustworthy AI for Multimodal Biomedical Imaging in Early Cancer Detection 截稿日期: 2026-06-30 Description The early detection of cancer remains one of the most effective strategies for improving survival rates and reducing treatment costs. Biomedical imaging modalities: such as mammography, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound, and dermoscopy; play a pivotal role in cancer screening, diagnosis, and monitoring. To broaden the applicability and societal impact of this research, this Special Issue expands its scope to include diverse cancer types such as breast, lung, brain, colon, liver, pancreatic, and skin cancers. These inclusions ensure that the findings and methodologies presented are representative of the wide spectrum of cancers encountered in clinical practice. Artificial intelligence (AI) and machine learning (ML) techniques have significantly advanced cancer imaging by improving image interpretation, pattern recognition, and predictive analysis, ultimately contributing to precision oncology. Despite these advances, major challenges persist in deploying AI-driven solutions in clinical environments. Many powerful AI systems operate as “black boxes,” generating predictions without explaining the rationale behind their decisions. This lack of interpretability can lead to reduced clinician trust and hesitancy in adoption. Moreover, AI models often suffer from issues related to data imbalance, limited generalizability across imaging modalities, and potential biases that may compromise fairness and robustness. Addressing these issues requires frameworks that emphasize not only high accuracy but also transparency, accountability, and clinical reliability. This Special Issue focuses on the development and integration of Explainable AI (XAI) and Trustworthy AI (TAI) methodologies for multimodal biomedical imaging in early cancer detection. Explainable AI (XAI) refers to computational techniques that make AI model decisions interpretable to humans, allowing clinicians to understand and validate model reasoning. Trustworthy AI (TAI) encompasses systems that ensure fairness, robustness, privacy, and accountability, fostering confidence in real-world clinical deployment. The issue will welcome contributions leveraging deep learning, fuzzy logic, neural networks, and hybrid AI approaches for interpretable and dependable cancer diagnosis. By bridging computational innovation and medical application, this Special Issue aims to promote multidisciplinary collaboration and advance AI-driven diagnostic frameworks that are transparent, fair, and clinically applicable. Common XAI techniques include: • SHAP (SHapley Additive exPlanations): Quantifies the contribution of each input feature (e.g., pixel intensity, region of interest) to the model’s output, enabling local and global interpretability. • LIME (Local Interpretable Model-Agnostic Explanations): Provides simple, interpretable models (e.g., linear approximations) around individual predictions to show which factors most influenced a decision. • Grad-CAM (Gradient-weighted Class Activation Mapping): Generates heatmaps highlighting critical regions in medical images that contributed to the model’s prediction. • Integrated Gradients and Attention Mechanisms: Offer gradient-based and model-intrinsic interpretability to enhance clinical trust and visual transparency. By applying these techniques, XAI ensures that clinicians can validate model reasoning, detect potential biases, and understand the diagnostic rationale—facilitating greater confidence in AI-assisted decision-making. Key aspects of Trustworthy AI (TAI) include: • Robustness and Reliability: The model performs consistently across different imaging modalities, scanners, and demographic groups. • Fairness and Bias Mitigation: Techniques are employed to prevent discrimination based on gender, race, or socioeconomic factors. • Data Security and Privacy Preservation: Federated learning and differential privacy methods protect patient information during AI training and deployment. • Ethical Accountability: Compliance with healthcare regulations and ethical AI frameworks ensures responsible use of data and algorithms. Together, XAI and TAI create a comprehensive foundation for developing interpretable, fair, and clinically dependable AI systems. Their integration into biomedical imaging research aligns technical innovation with ethical and regulatory standards, accelerating the safe translation of AI into clinical oncology practice. Potential topics include but are not limited to the following: Explainable AI frameworks for multimodal cancer imaging (e.g., MRI, CT, PET, ultrasound, dermoscopy) Trustworthy and robust AI systems for early detection across diverse cancer types (brain, lung, breast, colon, liver, pancreatic, skin, etc.) Methodological advancements using deep learning, fuzzy logic, neural networks, and hybrid AI models Integration of imaging data with genomic, pathological, and clinical metadata for predictive and personalized cancer modeling Quantitative evaluation of explainability, fairness, robustness, and generalizability of AI models in clinical settings Emerging imaging technologies and their AI-enhanced capabilities for early diagnosis Real-world deployment strategies and clinical translation of interpretable AI systems Ethical, regulatory, and privacy considerations in the development and adoption of AI-driven cancer diagnostics
由 Dou Sun 最后更新于
Special Issue on Machine Learning Approaches in Brain Tumor Segmentation 截稿日期: 2026-08-31 Description Biomedical imaging has emerged as an indispensable pillar of modern healthcare, enabling non-invasive visualization, diagnosis, and monitoring of a wide range of medical conditions. With the rapid evolution of imaging modalities such as MRI, CT, PET, ultrasound, and optical imaging, there is an unprecedented ability to capture high-resolution anatomical, functional, and molecular information. These advances, coupled with the integration of artificial intelligence (AI), machine learning (ML), and computational modeling, are revolutionizing the way clinicians interpret images, detect abnormalities, and tailor patient-specific treatment plans. Brain tumour segmentation plays a vital role in accurate diagnosis, treatment planning, and prognosis in neuro-oncology. With the rapid advancement of artificial intelligence, machine learning and deep learning techniques have emerged as powerful tools for automating the segmentation process from medical imaging data, particularly Magnetic Resonance Imaging (MRI). Despite these advancements, several challenges continue to hinder the full realization of biomedical imaging’s potential. High data complexity, variability in acquisition protocols, and the presence of noise and artifacts often limit the reproducibility and accuracy of image interpretation. Moreover, the integration of heterogeneous imaging data from multiple modalities requires sophisticated algorithms capable of handling large-scale, multidimensional datasets while ensuring interpretability and clinical relevance. Regulatory, ethical, and data privacy concerns also present barriers to the widespread adoption of AI-driven imaging solutions in clinical practice. Additionally, real-time processing demands in critical care, intraoperative guidance, and point-of-care diagnostics remain largely unmet, necessitating the development of more efficient and robust computational approaches. This Special Issue aims to bring together advanced research and innovative methodologies that address the challenges and opportunities in brain tumour segmentation using machine learning. We welcome contributions that explore novel algorithms, deep learning architectures, image preprocessing strategies, and feature extraction techniques for accurate and efficient segmentation of brain tumours from medical imaging modalities, particularly Magnetic Resonance Imaging (MRI). Topics of interest include AI-driven diagnostic frameworks, including convolutional neural networks (CNNs) and hybrid deep learning architectures, multimodal imaging fusion, automated tumour classification, and the integration of quantitative imaging biomarkers for clinical decision support. Special emphasis will be placed on approaches that enhance segmentation accuracy, improve robustness across diverse datasets, reduce computational complexity, and enable real-time analysis in clinical environments. By fostering collaboration between computer scientists, radiologists, and biomedical engineers, this Special Issue seeks to bridge the gap between algorithmic innovation and clinical applicability, ultimately contributing to early detection, precise treatment planning, and improved outcomes for patients with brain tumours. Potential topics include but are not limited to the following: Advanced image (particularly MRI) reconstruction and enhancement techniques CNN and deep learning architectures for precise delineation of tumor regions such as gliomas, meningiomas, and pituitary tumors Integrating automated brain tumor segmentation into clinical workflows Computational models for precision diagnostics and therapeutics
由 Dou Sun 最后更新于

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