ReviewJournal of medical systems2021
Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.
Review in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 92 citations in OpenAlex.
- A Hybrid Multimodal Cancer Diagnostic Framework Integrating Deep Learning of Histopathology and Whispering Gallery Mode Optical Sensors.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Quadra Sense: A Fusion of Deep Learning Classifiers for Mitosis Detection in Breast Cancer Histopathology.Diagnostics (Basel, Switzerland) · 2026Article
- Resilient Sinkhorn-Based Optimal Transport Late Fusion Framework for Breast Cancer Diagnosis.Cancer informatics · 2026Article
- Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.Scientific reports · 2025Article
- Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images.Bioengineering (Basel, Switzerland) · 2025Article
- Leveraging Attention-Based Deep Learning in Binary Classification for Early-Stage Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Semi-supervised tissue segmentation from histopathological images with consistency regularization and uncertainty estimation.Scientific reports · 2025Article
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Evaluation of the role of circular RNA (circ-FAF1) as a diagnostic biomarker for breast cancer in a cohort of Egyptian breast cancer patients.Molecular biology reports · 2025Article
- Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images.Journal of pathology informatics · 2025Article
- Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence.American journal of cancer research · 2025Article
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 2024Article
- [Research progress of breast pathology image diagnosis based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review.ACS sensors · 2024Review
- Expression and clinical significance of SYNE3 in non-small cell lung cancer.American journal of translational research · 2024Article
- Development of Automated Risk Stratification for Sporadic Odontogenic Keratocyst Whole Slide Images with an Attention-Based Image Sequence Analyzer.Diagnostics (Basel, Switzerland) · 2023Article
- WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- HistoML, a markup language for representation and exchange of histopathological features in pathology images.Scientific data · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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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.