Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

ESC core curriculum for the general cardiologist (2013)

[No abstract available

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples despite their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. To help the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we introduce the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modelling frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion

Systematic Analysis of Clinical Outcomes Following Stereotactic Radiosurgery for Central Neurocytoma.

Central neurocytoma (CN) typically presents as an intraventricular mass causing obstructive hydrocephalus. The first line of treatment is surgical resection with adjuvant conventional radiotherapy. Stereotactic radiosurgery (SRS) was proposed as an alternative therapy for CN because of its lower risk profile. The objective of this systematic analysis is to assess the efficacy of SRS for CN. A systematic analysis for CN treated with SRS was conducted in PubMed. Baseline patient characteristics and outcomes data were extracted. Heterogeneity and publication bias were also assessed. Univariate and multivariate linear regressions were used to test for correlations to the primary outcome: local control (LC). The estimated cumulative rate of LC was 92.2% (95% confidence interval: 86.5-95.7%, p<0.001). Mean follow-up time was 62.4 months (range 3-149 months). Heterogeneity and publication bias were insignificant. The univariate linear regression models for both mean tumor volume and mean dose were significantly correlated with improved LC (p<0.001). Our data suggests that SRS may be an effective and safe therapy for CN. However, the rarity of CN still limits the efficacy of a quantitative analysis. Future multi-institutional, randomized trials of CN patients should be considered to further elucidate this therapy

Magnetic resonance (MR) imaging of vascular malformations

Vascular malformations pose a diagnostic and therapeutic challenge due to the broad differential diagnosis as well as common utilization of inadequate or inaccurate classification systems among healthcare providers. Therapeutic approaches to these lesions vary based on the type, size, and extent of the vascular anomaly, necessitating accurate diagnosis and classification. Magnetic resonance (MR) imaging (MRI) is an effective modality for classifying vascular anomalies due to its ability to delineate the extent and anatomic relationship of the malformation to adjacent structures. In addition to anatomical mapping, the complete evaluation of vascular anomalies includes hemodynamic characterization. Dynamic time-resolved contrast-enhanced MR angiography provides information regarding hemodynamics of vascular anomalies, differentiating high- and low-flow vascular malformations. Radiologists must identify the MRI features of vascular malformations for better diagnosis and classification

Is attention all you need in medical image analysis? A review

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl