Special Issue "Advanced Trustworthy and Privacy Preserved Image Processing and Pattern Recognition Methods for Biomedical and Clinical Applications"
Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 10637
Interests: medical image analysis; multimodal information fusion; data synthesis; data harmonisation
Special Issues, Collections and Topics in MDPI journals
Interests: Medical Image Analysis; Segmentation, Data Synthesis; Federated Learning; Data Preprocessing
Interests: Medical Image Analysis; Data Science and Decisions; Deep and Federated Learning; Explainable AI
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
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.
- machine learning
- deep learning
- federated learning
- image synthesis
- transfer learning