ArticleBioengineering (Basel, Switzerland)2022
BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Deep learning model based on CT images to predict Ki-67 expression in renal carcinoma.Oncology letters · 2026Article
- Enhanced MobileNet with multi-scale feature fusion for automated breast cancer histopathology classification.Scientific reports · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable deep learning ensemble framework for accurate classification of wild poisonous mushroom species.BMC biotechnology · 2025Article
- Automated Computer-Assisted Diagnosis of Pleural Effusion in Chest X-Rays via Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.Scientific reports · 2025Article
- Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.Medicine · 2025Article
- Optimizing breast cancer ultrasound diagnosis: a comparative study of AI model performance and image resolution.Frontiers in oncology · 2025Article
- Accurate classification of benign and malignant breast tumors in ultrasound imaging with an enhanced deep learning model.Frontiers in bioengineering and biotechnology · 2025Article
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.Nature communications · 2024Article
- Article
- AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.Cancers · 2023Article
- Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.Quantitative imaging in medicine and surgery · 2023Article
- Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.Life (Basel, Switzerland) · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.
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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.