ArticleCancers2020
Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- MSWA-ResNet: Multi-Scale Wavelet Attention for Patient-Level and Interpretable Breast Cancer Histopathology Classification.Journal of imaging · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Development and validation of AI-based automatic segmentation and measurement of thymus on chest CT scans.BMC medical imaging · 2025Article
- Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model.Diagnostics (Basel, Switzerland) · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 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
- Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer.International journal of molecular sciences · 2024Review
- Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.Journal of personalized medicine · 2024Article
- Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies.NPJ breast cancer · 2022Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Classification of breast cancer histology images using MSMV-PFENet.Scientific reports · 2022Article
- A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology.Cancers · 2022Article
- An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma.Cancers · 2022Article
- ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network's dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.
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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.