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Advances in Image and Signal Processing and Biomedical Engineering Informatics
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Advances in Image and Signal Processing and Biomedical Engineering Informatics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 4215

Special Issue Editors

Prof. Dr. Yan Wang

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: computer vision;medical imaging;machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Zhang

E-Mail Website
Guest Editor
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
Interests: hyperspectral imaging; remote sensing; hyperspectral interdisciplinary applications

Special Issue Information

Dear Colleagues,

The Special Issue of “Advances in Image and Signal Processing and Biomedical Engineering Informatics” aims to highlight recent advancements in the fields of image and signal processing, as well as biomedical engineering and informatics. The original research published in this Special Issue is formerly presented at the conference CISP-BMEI 2024 (http://www.cisp-bmei.cn/). Researchers are encouraged to publish their work that presents novel methodologies, innovative applications, and interdisciplinary approaches. Emphasizing collaboration between image and signal processing and biomedical engineering informatics, this Special Issue focuses on fostering cross-disciplinary dialogue and accelerating scientific progress.

By providing a platform for academic exchange, this Special Issue aims to facilitate knowledge sharing, stimulate discussion on emerging challenges and opportunities, and foster collaborations among researchers worldwide. Manuscripts will undergo rigorous peer review to ensure the highest quality and relevance to the field.

Join us in advancing the frontiers of image and signal processing and biomedical engineering informatics and contribute to shaping the future of scientific research in these dynamic fields. We cordially invite submissions to this Special Issue, encompassing all facets of AI/ML/DL for the following:

  • Image and video processing;
  • Single processing;
  • Biomedical engineering;
  • Bioinformatics, systems biology, and medical informatics;
  • Fundamental technologies for medicine and biology.

Prof. Dr. Yan Wang
Dr. Qing Zhang
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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • image and signal processing
  • biomedical engineering
  • informatics
  • remote sensing
  • interdisciplinary research

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

19 pages, 19461 KiB  
Article
MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network
by Wenxiang Zhang, Chunmeng Wang and Jun Zhu
Sensors 2025, 25(8), 2500; https://doi.org/10.3390/s25082500 - 16 Apr 2025
Viewed by 253
Abstract
Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve [...] Read more.
Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve this problem, we propose a multi-exposure image fusion method based on a low-resolution context aggregation attention network (MEF-CAAN). First, we feed the low-resolution version of the input images to CAAN to predict their low-resolution weight maps. Then, the high-resolution weight maps are generated by guided filtering for upsampling (GFU). Finally, the high-resolution fused image is generated by a weighted summation operation. Our proposed network is unsupervised and adaptively adjusts the weights of channels to achieve better feature extraction. Experimental results show that our method outperforms existing state-of-the-art methods by both quantitative and qualitative evaluation. Full article
(This article belongs to the Special Issue Advances in Image and Signal Processing and Biomedical Engineering Informatics)
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17 pages, 4646 KiB  
Article
Mixed-Supervised Learning for Cell Classification
by Hao Sun, Danqi Guo and Zhao Chen
Sensors 2025, 25(4), 1207; https://doi.org/10.3390/s25041207 - 16 Feb 2025
Viewed by 552
Abstract
Cell classification based on histopathology images is crucial for tumor recognition and cancer diagnosis. Using deep learning, classification accuracy is hugely improved. Semi-supervised learning is an advanced deep learning approach that uses both labeled and unlabeled data. However, complex datasets that comprise diverse [...] Read more.
Cell classification based on histopathology images is crucial for tumor recognition and cancer diagnosis. Using deep learning, classification accuracy is hugely improved. Semi-supervised learning is an advanced deep learning approach that uses both labeled and unlabeled data. However, complex datasets that comprise diverse patterns may drive models towards learning harmful features. Therefore, it is useful to involve human guidance during training. Hence, we propose a mixed-supervised method incorporating semi-supervision and “human-in-the-loop” for cell classification. We design a sample selection mechanism that assigns highly confident unlabeled samples to automatic semi-supervised optimization and unreliable ones for online annotation correction. We use prior human annotations to pretrain the backbone and trustworthy pseudo labels and online human annotations to fine-tune the model for accurate cell classification. Experimental results show that the mixed-supervised model reaches overall accuracies as high as 86.56%, 99.33% and 74.12% on LUSC, BloodCell, and PanNuke datasets, respectively. Full article
(This article belongs to the Special Issue Advances in Image and Signal Processing and Biomedical Engineering Informatics)
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17 pages, 2689 KiB  
Article
Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle
by Nikita Pil and Alex G. Kuchumov
Sensors 2025, 25(1), 11; https://doi.org/10.3390/s25010011 - 24 Dec 2024
Viewed by 764
Abstract
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a [...] Read more.
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set. A method for the design of a synthetic database of geometric models is presented in this study. We suggest using synthetic geometries that enable the development of several aortic valve and left ventricular models in a range of sizes and shapes. In particular, we developed 22 variations of left ventricular geometries, including one original model, seven models with varying wall thicknesses, seven models with varying heights, and seven models with varying shapes. To guarantee anatomical accuracy and physiologically acceptable fluid volumes, these models were verified using actual patient data. Numerical simulations of left ventricle contraction and aortic valve leaflet opening/closing were performed to evaluate the electro-physiological potential distribution in the left ventricle and wall shear stress distribution in aortic valve leaflets. The proposed synthetic database aims to increase the predictive power of machine-learning models in cardiovascular research and, eventually, improve patient outcomes after aortic valve surgery. Full article
(This article belongs to the Special Issue Advances in Image and Signal Processing and Biomedical Engineering Informatics)
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15 pages, 363 KiB  
Article
A Data Ingestion Procedure towards a Medical Images Repository
by Mauricio Solar, Victor Castañeda, Ricardo Ñanculef, Lioubov Dombrovskaia and Mauricio Araya
Sensors 2024, 24(15), 4985; https://doi.org/10.3390/s24154985 - 1 Aug 2024
Cited by 2 | Viewed by 1258
Abstract
This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article [...] Read more.
This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports. Full article
(This article belongs to the Special Issue Advances in Image and Signal Processing and Biomedical Engineering Informatics)
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