ArticleScientific reports2022
Classification of breast cancer histology images using MSMV-PFENet.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ResViT-GANNet: a deep learning framework for classifying breast cancer histopathology images using multimodal attention and GAN-based augmentation.BMC medical imaging · 2025Article
- Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.European journal of cancer (Oxford, England : 1990) · 2025Article
- Enhancing bone radiology images classification through appropriate preprocessing: a deep learning and explainable artificial intelligence approach.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images.NPJ breast cancer · 2024Article
- Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.Life (Basel, Switzerland) · 2023Article
- Workflow for phenotyping sugar beet roots by automated evaluation of cell characteristics and tissue arrangement using digital image processing.Plant methods · 2023Article
- MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.Sensors (Basel, Switzerland) · 2023Article
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6 authors.
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Abstract
Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) for effective classification. With respect to the density of cell nuclei, we selected the regions potentially related to carcinogenesis at multiple scales from each view. The progressive feature encoding network then extracted the global and local features from these regions. A bidirectional long short-term memory analyzed the encoding vectors to get a category score, and finally the majority voting method integrated different views to classify the histopathological images. We tested our method on the breast cancer histology dataset from the ICIAR 2018 grand challenge. The proposed MSMV-PFENet achieved 93.0[Formula: see text] and 94.8[Formula: see text] accuracies at the patch and image levels, respectively. This method can potentially benefit the clinical cancer diagnosis.
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