ReviewVirchows Archiv : an international journal of pathology2019
Machine learning approaches for pathologic diagnosis.
Review in Virchows Archiv : an international journal of pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the predictive factors for the recurrence of acute pancreatitis.World journal of emergency surgery : WJES · 2025Pooled it
- Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning.JAAD international · 2026Article
- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Differences and Trends of Artificial Intelligence in Medical Education: A Comparative Bibliometric Analysis Between China and the International Community.Advances in medical education and practice · 2026Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- Use of artificial intelligence in the diagnosis of alterations in cervical cytology: A university population-based observational studyBiomedica : revista del Instituto Nacional de Salud · 2025Observational
- Web-based predictive tool for vaginal and vulvar melanomas: a machine learning study.Future science OA · 2025Article
- Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.Clinical and experimental dental research · 2025Article
- Glo-In-One-v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
- GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.Cancer reports (Hoboken, N.J.) · 2024Article
- Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.Diagnostic pathology · 2024Article
- Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification.iScience · 2024Article
- The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.BMC oral health · 2024Article
- Neighboring macrophage-induced alteration in the phenotype of colorectal cancer cells in the tumor budding area.Cancer cell international · 2024Article
- Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.Scientific reports · 2024Article
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition Challenge. Recently, such techniques have also been applied to various medical, including histopathological, images to assist the process of medical diagnosis. In some cases, deep learning-based algorithms have already outperformed experienced pathologists for recognition of histopathological images. However, pathological images differ from general images in some aspects, and thus, machine learning of histopathological images requires specialized learning methods. Moreover, many pathologists are skeptical about the ability of deep learning technology to accurately recognize histopathological images because what the learned neural network recognizes is often indecipherable to humans. In this review, we first introduce various applications incorporating machine learning developed to assist the process of pathologic diagnosis, and then describe machine learning problems related to histopathological image analysis, and review potential ways to solve these problems.
Indexed as
Identifiers
31222375What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.