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J. Imaging
Special Issues
AI-Driven Advances in Computational Pathology
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AI-Driven Advances in Computational Pathology

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1072

Special Issue Editors

Dr. Yuming Jiang

E-Mail Website
Guest Editor
Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA
Interests: AI; precision oncology; medical image analysis; prognosis prediction
Dr. Wencheng Li

E-Mail Website
Guest Editor
Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
Interests: fine needle aspiration cytology; surgical pathology; molecular marker of cancer
Dr. Xiaorui Liu

E-Mail Website
Guest Editor
Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Interests: trustworthy artificial intelligence; large-scale machine learning; deep learning on graphs/language/vision; generative AI

Special Issue Information

Dear Colleagues,

Histological assessment of human tissue is crucial in detecting and treating cancer and other diseases. The integration of digital image analysis with artificial intelligence (AI) techniques has transformative potential, alleviating or solving some of the issues in precision medicine and computational pathology. While deep learning has emerged as a powerful AI method for medical image analysis, most current models are purely data-driven, often lacking interpretability and explainability. Incorporating disease pathobiology into these models can improve clinical trust and generalizability.

AI-augmented histopathological image analysis enhances diagnostic precision by processing complex imaging data at a granular level. These advanced algorithms reveal intricate patterns and biomarkers that may evade human observation, supporting early disease detection and accurate classification. By analyzing characteristics such as texture, shape, intensity, and spatial architecture, AI models provide quantitative insights that aid clinical decision-making. Beyond diagnostics, this technology is essential for predicting disease progression, guiding treatment strategies, and advancing personalized medicine.

Histopathological biomarkers play a pivotal role in patient stratification, resource allocation, and therapeutic planning. By leveraging AI and digital pathology, diagnostic methodologies gain in speed, accuracy, and scalability. Adopting these tools is both promising and necessary for enhancing disease prediction, optimizing clinical workflows, and addressing urgent challenges in cancer care. As healthcare evolves, these technologies hold the potential to improve patient outcomes, delivering more effective, individualized treatments worldwide.

Dr. Yuming Jiang
Dr. Wencheng Li
Dr. Xiaorui Liu
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. Journal of Imaging 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 1800 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

  • histopathological image analysis
  • precision oncology
  • computational pathology
  • artificial intelligence
  • explainability

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Published Papers (1 paper)

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Research

22 pages, 8764 KB  
Article
Multi-Class Classification of Breast Cancer Subtypes Using ResNet Architectures on Histopathological Images
by Akshat Desai and Rakeshkumar Mahto
J. Imaging 2025, 11(8), 284; https://doi.org/10.3390/jimaging11080284 - 21 Aug 2025
Viewed by 629
Abstract
Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer [...] Read more.
Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer variability. Therefore, this study explores a deep learning-based, multi-class classification framework for distinguishing breast cancer subtypes using convolutional neural networks (CNNs). Unlike previous work using the popular BreaKHis dataset, where binary classification models were applied, in this work, we differentiate eight histopathological subtypes: four benign (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) and four malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma). This work leverages transfer learning with ImageNet-pretrained ResNet architectures (ResNet-18, ResNet-34, and ResNet-50) and extensive data augmentation to enhance classification accuracy and robustness across magnifications. Among the ResNet models, ResNet-50 achieved the best performance, attaining a maximum accuracy of 92.42%, an AUC-ROC of 99.86%, and an average specificity of 98.61%. These findings validate the combined effectiveness of CNNs and transfer learning in capturing fine-grained histopathological features required for accurate breast cancer subtype classification. Full article
(This article belongs to the Special Issue AI-Driven Advances in Computational Pathology)
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