Special Issue "Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis"
A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".
Deadline for manuscript submissions: 28 February 2022.
Special Issue Editors
Interests: pattern recognition; visual saliency; visual attention; biomedical imaging; human-computer interaction; crowd behaviour analysis; remote sensing; deep learning
Interests: machine learning; medical image analysis; knowledge discovery; pattern recognition
Interests: security in wireless networks; artificial intelligence; blockchain technology; pattern recognition
Interests: intelligent systems; data security; networks; Internet of Things (IoT); big data analysis; machine learning algorithms
Special Issues and Collections in MDPI journals
2. School of Computing and Information Sciences, Anglia Ruskin University, Cambridge CB1 1PT, UK
Interests: image processing; medical image analysis; artificial intelligence
Interests: big data; cloud computing; data analytics; deep learning; diabetic retinopathy; digital image processing; mammography; intrusion detection system; machine learning
Special Issue Information
Dear Colleagues,
An increased demand for accurate biomedical data analysis tools has recently encouraged both the scientific community and industry to develop increasingly sophisticated techniques for clinical data analysis. This Special Issue focuses on three main topics: computer vision (CV), Artificial Intelligence (AI), and the Internet of Medical Things (IoMT), with applications to mammographic image analysis.
Breast screening techniques such as mammography, MRI, CT, and digital breast tomosynthesis (DBT) represent the most widely adopted clinical exams worldwide for detecting masses, microcalcifications, lesions, spiculated regions, and other types of suspicious regions. However, many radiologists are used to examining several images daily, and it can also be particularly challenging to detect cancers in dense breasts (BI-RADS D).
Radiologists and practitioners also provide textual reports describing the severity of tumours, if any. CV and natural language processing (NLP) techniques build upon DL, allowing stratification of the information pipeline extracted from images and medical reports. NLP systems could play a vital role in building a semantic knowledge base from unstructured text and encoding clinical information from mammograms and MRI reports.
Hardware and software applications are currently emerging to make versatile healthcare devices with cloud computing to effectively utilise computational resources [1,2]. IoMT models such as electrocardiogram architecture for patient monitoring and smartphone applications have been proposed for e-healthcare systems. Breast density classification based on IoMT represents a recent trend development fostering the integration of AI and IoMT techniques.
This issue will invite reviews, original empirical investigations, manuscripts showcasing innovative ideas, methods and applications involving computer vision and machine learning (classical, biological, and hybrid) methods to varying facets of mammographic image analysis. In addition, the Special Issue aims to feature papers on the recently evolving fields of “radiomics”, IoMT, CV, NLP, and DL—where contemporary computing methods are applied to identify radiomics biomarkers and extract “semantic” features for the digital decoding and understanding of medical images.
Scientific References
[1] Darwish, Ashraf and Hassanien, Aboul Ella and Elhoseny, Mohamed and Sangaiah, Arun Kumar and Muhammad, Khan. The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems, Journal of Ambient Intelligence and Humanized Computing, vol.10, number 10, pages 4151--4166, (2019), Springer
[2] Aceto, Giuseppe and Persico, Valerio and Pescapé, Antonio. Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0, Journal of Industrial Information Integration, vol. 18, pages 100129, (2020), Elseiver
Dr. Alessandro Bruno
Dr. Pradeep N
Dr. Mangesh M. Ghonge
Dr. Mohamed Elhoseny
Dr. Faraz Janan
Dr. Gagandeep Jagdev
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- mammogram analysis
- computer vision
- image analysis
- segmentation
- classification
- unsupervised and supervised deep learning architectures
- generative adversarial networks
- variational autoencoders
- natural language processing
- data mining
- IoMT (Internet of Medical Things)
