ArticlePhysics in medicine and biology2017
Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.bioRxiv : the preprint server for biology · 2026Article
- Prediction of hormone receptor status in breast cancer brain metastases using an MRI-based multimodal deep learning framework.Frontiers in human neuroscience · 2026Article
- Artificial intelligence-driven multivariate integration for pulmonary arterial pressure prediction in pulmonary hypertension.NPJ digital medicine · 2025Article
- A hybrid deep learning approach with progressive cyclical CNN and firebug swarm optimization for breast cancer detection.PeerJ. Computer science · 2025Article
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Application of convolutional neural networks in medical images: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Review
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Ipsilateral Lesion Detection Refinement for Tomosynthesis.IEEE transactions on medical imaging · 2023Article
- Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records.Diagnostics (Basel, Switzerland) · 2023Article
- A Breast Cancer Image Classification Algorithm with 2c Multiclass Support Vector Machine.Journal of healthcare engineering · 2023Article
- Convolutional neural network -based phantom image scoring for mammography quality control.BMC medical imaging · 2022Article
- A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.Sensors (Basel, Switzerland) · 2022Article
- Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis.NPJ digital medicine · 2022Article
- ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- A holistic overview of deep learning approach in medical imaging.Multimedia systems · 2022Article
- Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.Joint diseases and related surgery · 2022Article
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6 authors.
Funding
Abstract
Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.
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