Image Analysis and Machine Learning in Cancers: 2nd Edition
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 16
Special Issue Editors
Interests: medical imaging (mammography and digital breast tomosynthesis); machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: image analysis; machine learning; medical applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the years, we have seen tremendous advances in image analysis and machine learning (ML) techniques for cancer detection. This phenomenon has been powered mainly by better equipment capturing higher-quality data, the availability of public datasets, and advances in computer technology that have enabled us to use methods that were once inaccessible. For example, we have seen many papers addressing super-resolution images, fusing images from different modalities to achieve better diagnosis, and even generating more data based on a limited available number.
Imaging processing techniques form the basis of all artificial intelligence (AI)-based systems. It has been proven that pre-processing methods have a substantial impact on the next steps in an ML/AI pipeline. Therefore, it is not surprising to see many papers proposing new methods to enhance images even further to achieve better results.
Machine learning approaches, also known as conventional approaches, have been essential in pushing boundaries in the detection and diagnosis of cancers. Since they can work with limited datasets and more modest computers, as opposed to deep learning approaches, many methods have been proposed since the popularization of AI. Nowadays, these methods can compete with DL-based ones in terms of accuracy, specificity, and sensitivity.
Deep learning (DL) techniques, developed based on the increased capabilities and accessibility of more powerful hardware, play an important role in this scenario. Since their basis lies in imaging analysis and machine learning, recent advances have shown many options for ensuring good diagnosis, reducing the length of follow-up in patients. However, DL models are known for their lack of explainability, although some studies have proposed ways to overcome this.
This Special Issue is dedicated to sharing the most recent advances in image analysis and machine learning techniques to achieve better detection/diagnosis in cancers. You are welcome to read the publications in the first edition at https://www.mdpi.com/journal/cancers/special_issues/WHG4FRJH4A.
Dr. Helder C. R. De Oliveira
Dr. Arianna Mencattini
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 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
- cancer detection and diagnosis system
- machine learning
- deep learning
- medical imaging analysis
- few-shot deep learning
- attention segmentation
- feature extraction
- probabilistic models
- explainability
- image fusion
- generative adversarial network
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
