Statistics and Data Science in Imaging

Statistical Methods for Biomedical Imaging Data

We invite submissions to a peer-reviewed special issue on “Statistical Methods for Biomedical Imaging Data” to advance the development of statistical tools for modern multimodal biomedical data. This issue will focus on the integration of radiology, pathology, and spatially resolved molecular profiling data, including CODEX and spatial transcriptomics.

Topics include multimodal data fusion, spatial and functional data analysis, data harmonization, scalable methods for complex high-dimensional imaging data, and uncertainty-aware inference. We encourage contributions exploring the use of foundation models and other machine learning approaches within principled statistical frameworks, with attention to predictive efficacy, interpretability, scalability, robustness, and reproducibility. Applications to cancer biology and clinical oncology are especially encouraged.

SDSI publications currently have no APCs (article publishing charges).

In addition to the types of articles published by the journal as described below, short papers on high-quality research can also be submitted for consideration in this special issue. Manuscripts submitted must meet the same requirements and standards as regular submissions. The SDSI Editor will conduct initial screenings of all submissions and make assignments to the Guest Editors, who will oversee the peer-review process. To ensure efficiency in the review and decision-making process while maintaining a high standard of academic rigor, we aim for a review time not exceeding 10 weeks from submission to first decision.

Statistics and Data Science in Imaging is published on behalf of the ASA with support from its Section on Statistics in Imaging. It is an international open access journal and publishes only open access articles. SDSI articles have free and immediate online access upon publication, increasing the visibility, readership, and impact of your research.

The journal publishes original research and reviews on the Statistical Analysis of Imaging Data. The primary aim of the journal is to serve as a forum for discussing methodological challenges encountered in the analysis of imaging data and for presenting statistically sound solutions to those challenges. The journal covers a broad spectrum of statistical methods and data science techniques applicable to various imaging domains, including, but not limited to, neuroimaging, medical imaging, satellite imaging, physics, forensic imaging, astronomy, remote sensing, and materials science. The target audience comprises quantitative researchers, including statisticians, engineers, computer scientists, and data scientists, along with imaging researchers in fields such as brain science, radiology, satellite, forensic imaging, environmental studies, who are involved in developing and investigating methods for analyzing imaging data.

Along with methodological research papers, the journal publishes discussion papers, soliciting concise feedback from the statistical imaging community, in-depth reviews of specific topics by leading statisticians and data scientists, case-study papers that highlight applications of statistical methods in imaging data analysis to address real world questions, short communications highlight emerging issues of interest to the statistical community, and best practices papers for pipelines of data pre-processing and analysis to facilitate access to public data repositories and use of statistical software.

For further inquiries about the SDSI Special Issue, contact the SDSI Editor Michele Guindani or any of the guest Editors.