ArticleBioinformatics (Oxford, England)2022
Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Explainable CNN with spatial attention for oral cancer patch selection using a custom scoring metric.Biomedizinische Technik. Biomedical engineering · 2026Article
- Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.PloS one · 2026Article
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
- Predicting breast cancer treatment response and prognosis using AI-based image classification.Frontiers in oncology · 2025Article
- Article
- An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm.PloS one · 2024Article
- Article
- Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images.Diagnostics (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
motivationBreast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets.
resultsIn this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification. AVAILABILITY AND IMPLEMENTATION: Software is publicly available at: https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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