ArticleIEEE journal of biomedical and health informatics2021
Distant Domain Transfer Learning for Medical Imaging.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring.IEEE open journal of engineering in medicine and biology · 2025Article
- XAI-FusionNet: Diabetic foot ulcer detection based on multi-scale feature fusion with explainable artificial intelligence.Heliyon · 2024Article
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer's disease to Parkinson's disease.International journal of computer assisted radiology and surgery · 2023Article
- Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning for the screening of primary ciliary dyskinesia based on cranial computed tomography.Frontiers in physiology · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Improved transfer learning using textural features conflation and dynamically fine-tuned layers.PeerJ. Computer science · 2023Article
- Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Convolutional neural network based CT scan classification method for COVID-19 test validation.Smart health (Amsterdam, Netherlands) · 2022Article
- A Novel Distant Domain Transfer Learning Framework for Thyroid Image Classification.Neural processing letters · 2022Article
- Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things.Computational intelligence and neuroscience · 2022Article
- Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things.Computational intelligence and neuroscience · 2022Article
- Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.Applied soft computing · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.
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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.