ArticleCancers2022
Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.Physical and engineering sciences in medicine · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Detection of Masses in Mammogram Images Based on the Enhanced RetinaNet Network With INbreast Dataset.Journal of multidisciplinary healthcare · 2025Article
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- Image analysis and teaching strategy optimization of folk dance training based on the deep neural network.Scientific reports · 2024Article
- Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.Cancers · 2024Article
- Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.BJR open · 2024Review
- A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.Frontiers in oncology · 2024Article
- MediNet: transfer learning approach with MediNet medical visual database.Multimedia tools and applications · 2023Article
- Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.Journal of digital imaging · 2023Article
- Vision-Transformer-Based Transfer Learning for Mammogram Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images.Cancers · 2022Article
- BUViTNet: Breast Ultrasound Detection via Vision Transformers.Diagnostics (Basel, Switzerland) · 2022Article
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3 authors.
Funding
Abstract
Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a
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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.