Featured Articles

  • Position Paper Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings
    Vol. 23, Issue 2

    DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for skin lesion diagnosis

    Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of Convolutional Neural Networks(CNNs)in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based Computer Aided Diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists, aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.

  • Vol. 23, Issue 2

    Position Paper
    Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings

    Stress and mental health have become major concerns worldwide. Research has already extensively investigated physiological signals as quantitative and continuous markers of stress. In recent years the focus of the field has shifted from the laboratory to the ambulatory environment. We provide an overview of physiological stress detection in laboratory settings with a focus on identifying physiological sensing priorities, including electrocardiogram, skin conductance and electromyogram, and the most suitable machine learning techniques, of which the choice depends on the context of the application. Additionally, an overview is given of new challenges ahead to move towards the ambulant environment, including the influence of physical activity, lower signal quality due to motion artifacts, the lack of a stress reference and the subject-dependent nature of the physiological stress response. Finally, several recommendations for future research are listed, focusing on large scale, longitudinal trials across different population groups and just-in-time interventions to move towards disease prevention and interception.

  • Vol. 23, Issue 2

    Review Paper on Dermoscopy Image Analysis: Overview and Future Directions

    Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors, the field progressed rather slowly in its first two decades.

  • Special Issue on Skin Lesion Image Analysis for Melanoma Detection
    Vol. 23, Issue 2

    Special Issue on Skin Lesion Image Analysis for Melanoma Detection

    Special Issue on Skin Lesion Image Analysis for Melanoma Detection in Vol. 23, Issue 2

  • Vol. 23, Issue 2

    Sensor Informatics

    Sensor Informatics articles in Vol. 23, Issue 2

  • Imaging Informatics
    Vol. 23, Issue 2

    Imaging Informatics

    Imaging Informatics articles in Vol. 23, Issue 2

  • Medical Informatics
    Vol. 23, Issue 2

    Medical Informatics

    Medical Informatics articles in Vol. 23, Issue 2

  • Position Paper on Computational Cardiology
    Vol. 23, Issue 1

    Position Paper on Computational Cardiology

    Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling and cardiovascular informatics at the intersection of atherogenesis and vascular biology.

  • Special Section on Integrating Informatics and Technology for Precision Medicine
    Vol. 23, Issue 1

    Special Section on Integrating Informatics and Technology for Precision Medicine

    Special Section on Integrating Informatics and Technology for Precision Medicine in Vol. 23, Issue 1

  • Vol. 23, Issue 1

    Special Section on Biomedical Data Learning, Reasoning, and Representation

    Special Section on Biomedical Data Learning, Reasoning, and Representation in Vol. 23, Issue 1

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