AI-Driven Innovations in Computational Histology/Pathology
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 590
Special Issue Editors
Interests: virtual staining; multi-instance learning; self-supervised learning; pathology foundation models; renal pathology; breast pathology
Interests: cancer metabolism; epigenetics; mTOR complex; glioblastoma; molecular pathology; molecular pathology of brain tumors; neuropathology; experimental pathology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rapid advancements in artificial intelligence (AI) have opened up new frontiers in the field of computational histology/pathology, revolutionizing diagnostic accuracy, efficiency, and the understanding of complex medical data. In this context, the integration of cutting-edge techniques such as virtual staining, multi-instance learning, self-supervised learning, and pathology foundation models is pivotal for enhancing both research and clinical practice.
This Special Issue on “AI-Driven Innovations in Computational Histology/Pathology” will highlight original research articles and comprehensive reviews that explore and expand on these novel approaches to leverage AI for pathology applications. It will discuss breakthroughs that could transform traditional histology/pathology workflows and unlock new possibilities in precision medicine.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Generative Models: Techniques and applications of digital and AI-based generative models. Virtual staining, generative image augmentation, and large-language model (LLM) applications are within this field;
- Multi-Instance Learning (MIL): Innovative MIL models designed to analyze complex pathological images, providing robust predictions in scenarios with limited annotations and heterogeneous data;
- Self-Supervised Learning and Pathology Foundation Models: Approaches in self-supervised learning that facilitate the training of models with minimal labeled data, as well as the application of large-scale foundation models;
- Comparison Studies and Case Studies: The evaluation of traditional versus AI-enhanced methods of tissue analysis and diagnostics. Real-world applications of these advanced AI techniques to clinical cases and their outcomes are also discussed.
We welcome contributions that push the boundaries of current computational methods and contribute to the growing body of knowledge, aiming to enhance diagnostic precision and streamline histology/pathology practices. Research that incorporates experimentation and data-driven simulation, as well as reviews that synthesize these areas, are highly encouraged.
We look forward to receiving your submissions and are excited to feature groundbreaking work that will shape the future of histology/pathology through AI innovations.
Dr. Masataka Kawai
Dr. Kenta Masui
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 short 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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
- generative models
- multi-instance learning
- self-supervised learning
- pathology foundation models
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