Recent advances in digital pathology and computational imaging have significantly enhanced our ability to analyze complex tissue structures in neuroscience and beyond. High-resolution whole-slide images (WSIs), combined with machine learning and deep learning techniques, are enabling more objective, reproducible, and scalable insights into disease mechanisms. Despite these advancements, traditional computational methods often suffer from limited generalizability, heavy annotation requirements, and a lack of interpretability in clinical contexts.
In parallel, the emergence of large-scale foundation models in pathology, particularly pre-trained vision transformers and self-supervised learning approaches, has opened new avenues for more robust and transferable analysis across different datasets and diseases. While their application in neuroscience is still emerging, these models offer valuable tools for accelerating biomarker discovery, improving clinical decision support, and understanding disease pathology at scale. There is a growing need to explore how these models and new algorithmic frameworks can be effectively applied to both general and neuroscience-specific pathological image analysis.
This Research Topic aims to bring together recent advances in computational pathology with a special focus on neuroscience applications, while remaining open to broader contributions that demonstrate methodological or clinical innovation. We welcome original research, reviews, and perspectives that cover the full spectrum of pathological image analysis from algorithm development to clinical translation and biological discovery.
We particularly encourage submissions addressing:
- Robust and interpretable deep learning methods for analyzing histopathological images
- Novel approaches for biomarker discovery and disease stratification
- Integration of computational pathology with clinical outcomes and multi-omics data
- Development and validation of foundation models or transfer learning strategies across diverse pathology domains
- New pipelines or tools for efficient annotation, data standardization, and deployment in real-world scenarios
We invite high-quality submissions in (but not limited to) the following areas:
- Computational methods for histopathological image analysis in neuroscience and general pathology
- Disease classification, prognosis, and subtype discovery using AI-driven approaches
- Biomarker discovery from pathology images and integration with genomics/clinical data
- Cross-modal learning: pathology images with radiology, omics, or text
- Dataset curation, annotation strategies, and reproducible pipelines
- Clinical validation, interpretability, and deployment of AI in pathology workflows
- Federated learning or privacy-preserving analysis for multi-center collaborations
All submissions should clearly articulate their relevance to pathology and demonstrate a contribution to advancing the field, whether through algorithmic novelty, clinical insight, or translational value.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Clinical Trial
Community Case Study
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
