Artificial Intelligence in Computational Pathology for Cancer Diagnosis

Topic Information

Dear Colleagues,

Artificial intelligence is rapidly transforming computational pathology, revolutionizing cancer diagnosis through the advanced analysis of digitized histopathology images. The integration of deep learning methods, including convolutional neural networks and foundation models, has enabled unprecedented capabilities in tissue classification, tumor detection, biomarker prediction, and prognostic assessment. Recent developments in vision transformers and foundation models trained on millions of whole-slide images demonstrate remarkable performance in both common and rare cancer detection, while multimodal AI approaches are advancing precision oncology by integrating histomorphological features with genomic and clinical data.

This Topic aims to showcase cutting-edge research and comprehensive reviews on the application of artificial intelligence in computational pathology for cancer diagnosis. We welcome original contributions that address diagnostic accuracy, prognostic modeling, biomarker discovery, tumor microenvironment analysis, and clinical integration of AI-driven tools. The scope encompasses diverse cancer types and computational approaches, from traditional machine learning to state-of-the-art foundation models, emphasizing clinically validated methodologies that enhance diagnostic workflows and support precision medicine.

In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Deep learning architectures for histopathology image analysis;
• Foundation models and self-supervised learning in digital pathology;
• AI-based tumor detection, classification, and grading;
• Computational biomarker discovery and prediction;
• Prognostic and predictive modeling using histopathology images;
• Tumor microenvironment characterization and spatial analysis;
• The multi-modal integration of pathology, genomics, and clinical data;
• Clinical validation and regulatory aspects of AI in pathology;
• Explainable AI and interpretability in cancer diagnosis;
• Quality control and standardization in computational pathology.

We look forward to receiving your contributions and hope to advance this rapidly evolving field.

Dr. Md Mamunur Rahaman
Prof. Dr. Yu-Dong Yao
Dr. Chen Li
Dr. Jinghua Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Cancers cancers	4.4	8.8	2009	19.1 Days	CHF 2900	Submit
Current Oncology curroncol	3.4	4.9	1994	22.8 Days	CHF 2200	Submit
Diagnostics diagnostics	3.3	5.9	2011	21.6 Days	CHF 2600	Submit

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