ArticleAcademic radiology2023
Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.
Article in Academic radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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16 citing papers in PubMed, 2 syntheses or guidelines 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-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.European radiology · 2025Pooled it
- Deep Learning for Early Breast Cancer Detection on Contrast-Enhanced Breast MRI: A Multicenter Study.Cancers · 2026Article
- Multimodal Deep Learning Based on Ultrasound Images and Clinical Data for Better Ovarian Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- SwinCAMF-Net: Explainable Cross-Attention Multimodal Swin Network for Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluating Surgical Results in Breast Cancer with Artificial Intelligence.Aesthetic plastic surgery · 2025Article
- High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.Radiology. Artificial intelligence · 2025Article
- An ultra-wide-field fundus image dataset for intelligent diagnosis of intraocular tumors.Scientific data · 2025Article
- A whole slide image-based risk score predicts prognosis and postmastectomy radiotherapy benefit in triple negative breast cancer patients.Scientific reports · 2025Article
- Machine Learning Based on Digital Mammography to Reduce the Need for Invasive Biopsies of Benign Calcifications Classified in BI-RADS Category 4.Journal of imaging informatics in medicine · 2025Article
- An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.Scientific reports · 2025Article
- Deep learning for the detection of moyamoya angiopathy using T2-weighted images: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer.Life (Basel, Switzerland) · 2025Article
- Microbiota-host metabolism reprogramming in colorectal cancer: from pathogenesis to precision therapies.Frontiers in oncology · 2025Review
- Automatic classification of fungal-fungal interactions using deep leaning models.Computational and structural biotechnology journal · 2024Article
- Temporal convolutional network for a Fast DNA mutation detection in breast cancer data.PloS one · 2023Article
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12 authors.
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Abstract
RATIONALE AND
objectivesDiagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND
methodsTwo datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis.
resultsIn the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant.
conclusionResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.
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