Computational intelligence (CI) in medical imaging is a growing field that employs artificial intelligence (AI) techniques to analyze and process medical images with the goal of improving diagnosis, early disease detection, and treatment monitoring. The most popular techniques and approaches in this field include using Deep Learning and Evolutionary Algorithms.  This track aims to generate implementations that present single or hybrid computational intelligence methods for solving problems in medical image processing and computer vision.

The special track will be an excellent opportunity for researchers working on CI in medical imaging to exchange their recent ideas and investigations on this topic. In this respect, we welcome high-quality papers on the theoretical, developmental, implementational, and application of CI approaches in medical imaging. More particularly, the special track will encourage original research contributions that address new and existing IC approaches and related methodologies to be employed in the field of medical imaging.

Topics of interest

The topics of interest in this special track include (but are not limited to) the following topics:

  • Image Segmentation: Segmentation refers to the task of dividing an image into meaningful regions. In medical images, segmentation is essential to identify and delineate anatomical or pathological structures. CI-based segmentation algorithms can help in accurately identifying specific areas of interest. 
  • Detection of Anomalies and Early Markers: Computational intelligence approaches can identify patterns that differ from normality and alert healthcare professionals to potential problems.
  • Diagnosis and Classification: Machine learning algorithms can be trained to diagnose diseases or medical conditions based on images. This may include detecting diseases on X-rays, magnetic resonance imaging (MRI), computed tomography (CT) scans, and other types of medical imaging.
  • Image Registration: To facilitate correlation and comparison, image registration involves aligning and comparing medical images from different times or modalities. Computational intelligence methods can improve the accuracy and efficiency of these processes.
  • Computer-Aided Diagnosis (CAD): CAD systems use artificial intelligence algorithms to assist radiologists and other healthcare professionals in interpreting medical images. They can provide objective analysis and early detection of pathologies.
  • Synthetic Image Generation: Synthetic image generation creates larger and more varied data sets. This is especially useful when you have limited data sets, as it can improve the generalization ability of CI models.
  • Integration with Clinical Data: Integrating imaging data with additional clinical information can improve diagnostic accuracy and provide a more complete understanding of the patient’s condition.

Expected types of contributions

  • Regular papers: The length of the contribution is limited to 6 pages, but it is possible to extend the paper length up to 8 pages by paying for each extra page. Check fees for more information.
  • Short papers: The length of the contribution is limited to 4 pages and no less than 3 pages, not being possible to extend the paper length. The duration of the oral presentation of short posters will be less than regular ones.
  • Posters: The length of the contribution is limited to 2 pages. Poster papers will be included in the proceedings but won’t include oral presentations during the conference. The authors of a poster also need to prepare a real poster to be shown during the conference. For presentation purposes at the conference, the authors must prepare the poster in portrait format. The accepted dimensions are 60 (width) x 80 (length).

Accepted special track papers are published in the conference proceedings in the workshop section, available online via IEEE, and indexed by IEEE Xplore.

More information available at: https://2025.cbms-conference.org/

Organizers

  • Dr. Saúl Zapotecas Martínez, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico
  • Dr. Diego Oliva, Universidad de Guadalajara, Mexico 
  • Dra. Raquel Díaz Hernández, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico
  • Dr. Leopoldo Altamirano-Robles, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico

Program Committee

  • Dr. Mohamed Abd Elaziz, Zagazig University, Egypt
  • Dra. Sandra Balderas-Mata, Universidad de Guadalajara, México
  • Dra. Itzel Aranguren, Universidad de Guadalajara, México
  • Dr. Seyed Jalaleddin Mousavirad, Mid Sweden University, Sweden
  • Dr. Gonzalo Pajares, Universidad Complutense de Madrid, Spain
  • Dr. Alejandro Rosales Pérez, Centro de Investigación en Matemáticas, Mexico
  • Dr. Mohammad H. Nadimi-Shahraki, Islamic Azad University, Iran
  • Dr. Jesús Guillermo Falcón, Tecnológico de Monterrey, Mexico
  • Dr. Bilel Derbel, INRIA-Lille, France
  • Dra. Adriana Menchaca Méndez, Universidad Nacional Autónoma de México, Mexico
  • Dr. Ram Sarkar, Jadavpur University, India

Contact

For more information, please visit the IEEE CBMS 2025 website at https://2025.cbms-conference.org/ or contact the track chairs at szapotecas [at] inaoe [dot] mx.

Call for papers

You can download Call for papers here.