ArticleComputer methods and programs in biomedicine2012
Histology image analysis for carcinoma detection and grading.
Article in Computer methods and programs in biomedicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.
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Who cites it
97 citing papers in PubMed, 1 synthesis or guideline pooled it, 378 citations in OpenAlex.
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- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- [Feature distillation multiple instance learning method based on sequence reorganized Mamba].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
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- A novel expert-annotated single-cell dataset for thyroid cancer diagnosis with deep learning benchmarks.PLOS digital health · 2025Article
- A probabilistic detection-based approach to skin and freckle segmentation.Scientific reports · 2025Article
- A multi-phase framework for enhancing diagnostic accuracy and transparency in renal cell carcinoma grading using YOLOv8 and GradCAM.Scientific reports · 2025Article
- OncoMet: a deep learning framework for the prediction of oncogenic signaling pathways and metastasis in esophageal cancer patients using histopathology images from primary tumors.Journal of translational medicine · 2025Article
- CAMIL: channel attention-based multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2025Article
- Disappearing colorectal liver metastases: the importance of radiographic-pathologic correlation in oncology care.Hepatobiliary surgery and nutrition · 2025Article
- Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images.Visual computing for industry, biomedicine, and art · 2025Article
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37 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.
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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.