ArticleSensors (Basel, Switzerland)2023
Breast Cancer Histopathological Images Segmentation Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- DenseUNet for Breast Cancer Segmentation in Histopathological Images.Journal of medical signals and sensors · 2026Article
- Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning.Scientific reports · 2025Article
- MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors.Quantitative imaging in medicine and surgery · 2025Article
- Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.PloS one · 2025Article
- HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction.Heliyon · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which remains a costly and difficult task. Thus, the use of an unsupervised segmentation method could facilitate the process. In this article, we propose two approaches for the semantic segmentation of breast cancer histopathology images. On the one hand, an autoencoder architecture for unsupervised segmentation is proposed, and on the other hand, an improvement U-Net architecture for supervised segmentation is proposed. We evaluate these models on a public dataset of histological images of breast cancer. In addition, the performance of our segmentation methods is measured using several evaluation metrics such as accuracy, recall, precision and F1 score. The results are competitive with those of other modern methods.
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Registered trials
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