sensors-logo

Journal Browser

Journal Browser

Special Issue "Advanced Trustworthy and Privacy Preserved Image Processing and Pattern Recognition Methods for Biomedical and Clinical Applications"

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

Deadline for manuscript submissions: 15 May 2022.

Special Issue Editors

Dr. Guang Yang
E-Mail Website1 Website2 Website3
Guest Editor
National Heart and Lung Institute, Imperial College London, South Kensington, London SW7 2AZ, UK
Interests: Medical Image Analysis; Multimodal Information Fusion; Data Synthesis; Data Harmonisation
Dr. Oliver Diaz
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Barcelona, 08007 Barcelona, Spain
Interests: Medical Image Analysis; Segmentation, Data Synthesis; Federated Learning; Data Preprocessing
Dr. Giorgos Papanastasiou
E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: Medical Image Analysis; Data Science and Decisions; Deep and Federated Learning; Explainable AI
Dr. Karim Lekadir
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Barcelona, 08007 Barcelona, Spain
Interests: medical image analysis; machine learning; predictive modelling; data management

Special Issue Information

Dear Colleagues,

In complex clinical decision-making settings, several collaborative learning frameworks have recently been investigated for enhancing the application of machine and deep learning algorithm effectiveness. For example, current federated learning techniques enable privacy-preserving model training across different sites with distributed data storage, model compression improves the applicability of a trained model to different edge devices with different computing power, and the performance and robustness of independently trained models can be improved using state-of-the-art knowledge distillation methods. The development of explainable AI and transfer learning is also making significant strides toward improving the transparency and transferability of the current machine and deep learning models. The goal of this Special Issue is to compile the most recent and advanced machine and deep learning research findings that aid in collaborative decision making for biomedical and clinical applications. Any effort to bridge the gap between various sensors, imaging devices, data, models, and machine and deep learning approaches across sites will be welcomed, but the relationship between machine and deep learning methods and downstream diagnostic objectives should be clearly stated.

We welcome articles including, but are not limited to the following topics:

  • Federated learning methods for biomedical imaging and post-processing;
  • Transfer learning and domain-adaptation;
  • Information fusion, joint prediction using multi-modality imaging and other types of data;
  • Object detection, recognition, classification, segmentation, registration and reconstruction, enhancement of biomedical imaging data;
  • Cross-modality image synthesis;
  • Transparency, explainability, privacy preserved and interpretability of deep learning models.

Dr. Guang Yang
Dr. Oliver Diaz
Dr. Giorgos Papanastasiou
Dr. Karim Lekadir
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. 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 2400 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

  • AI
  • machine learning
  • deep learning
  • federated learning
  • segmentation
  • detection
  • image synthesis
  • reconstruction
  • transfer learning
  • XAI

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Two-Stage Segmentation Framework Based on Distance Transformation
Sensors 2022, 22(1), 250; https://doi.org/10.3390/s22010250 - 30 Dec 2021
Viewed by 111
Abstract
With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in [...] Read more.
With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in medical images, making it challenging to achieve high-accuracy segmentation of lesions or organs. In this paper, we assume that the distance map obtained by performing distance transformation on the ROI edge can be used as a weight map to make the network pay more attention to the learning of the ROI edge region. To this end, we design a novel framework to flexibly embed the distance map into the two-stage network to improve left atrium MRI segmentation performance. Furthermore, a series of distance map generation methods are proposed and studied to reasonably explore how to express the weight of assisting network learning. We conduct thorough experiments to verify the effectiveness of the proposed segmentation framework, and experimental results demonstrate that our hypothesis is feasible. Full article
Show Figures

Figure 1

Article
DCNet: Densely Connected Deep Convolutional Encoder–Decoder Network for Nasopharyngeal Carcinoma Segmentation
Sensors 2021, 21(23), 7877; https://doi.org/10.3390/s21237877 - 26 Nov 2021
Viewed by 339
Abstract
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly [...] Read more.
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly unsatisfactory with indistinguishable and blurred boundaries of segmentation results of tiny tumor volume. To address the problem, we propose a densely connected deep convolutional network consisting of an encoder network and a corresponding decoder network, which extracts high-level semantic features from different levels and uses low-level spatial features concurrently to obtain fine-grained segmented masks. Skip-connection architecture is involved and modified to propagate spatial information to the decoder network. Preliminary experiments are conducted on 30 patients. Experimental results show our model outperforms all baseline models, with improvements of 4.17%. An ablation study is performed, and the effectiveness of the novel loss function is validated. Full article
Show Figures

Figure 1

Back to TopTop