Recent Advances of Deep Learning for Cancer Diagnosis and Prognosis
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 121
Special Issue Editors
Interests: genomics; bioinformatics; cancer
Interests: prognostic and predictive factors in breast cancer; immunotherapy in breast cancer; endocrine therapy in breast cancer
Interests: image interpolation; image superresolution; image processing; image enhancement; deep learning for image processing; deep learning for image analysis; image filtering
Special Issue Information
Dear Colleagues,
Deep learning has advanced substantially in recent years due to the availability of powerful computational resources, large datasets, and the invention of novel network architectures. Deep learning boasts successful applications in many fields, including the early detection of cancer and the prediction of cancer prognosis. As a complicated disease that depends on multiple factors, the accurate assessment of cancer subtype, onset, and development requires the integration of heterogeneous datasets such as genome sequencing data, medical image data, and electronic medical data or available clinical records to identify informative biomarkers and make reliable predictions. On the other hand, cancer data are relatively rare, primarily due to limited and controlled access to patient samples. Furthermore, to make actionable recommendations that will be acceptable to the medical community, the interpretability of a prediction model is also essential. As a result, the multi-faceted causes of cancer, complex interdependency of biological systems, heterogeneity of available data, limited data volume, expectation of high interpretability, privacy, and ethics all pose special challenges to the application of deep learning in this field. For this Special Issue, we welcome manuscripts that focus on addressing the above challenges from a technical or algorithmic perspective, as well as applications of existing deep learning methods that make substantial clinical impacts. We also welcome systematic reviews, original research, and privacy, ethics, economic evaluation, and cost–effectiveness studies on this relevant topic.
Dr. Cuncong Zhong
Prof. Dr. Marcus Schmidt
Dr. Olivier Rukundo
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. Cancers 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 2900 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
- deep learning
- cancer diagnosis
- cancer prognosis
- genome sequencing
- biomarker
- medical imaging
- electronic medical record
- data integration
- model interpretability
- ethics
