ArticleIEEE transactions on pattern analysis and machine intelligence2018
Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications.
Article in IEEE transactions on pattern analysis and machine intelligence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- PIV-FlowDiffuser: Transfer-Learning-Based Denoising Diffusion Models for Particle Image Velocimetry.Sensors (Basel, Switzerland) · 2025Article
- A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments.Diagnostics (Basel, Switzerland) · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.BMC medical informatics and decision making · 2023Article
- Application of digital pathology and machine learning in the liver, kidney and lung diseases.Journal of pathology informatics · 2023Review
- Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005-2012.Ecotoxicology and environmental safety · 2022Article
- Artificial intelligence in glomerular diseases.Pediatric nephrology (Berlin, Germany) · 2022Review
- Transfer learning for automatic joint segmentation of thyroid and breast lesions from ultrasound images.International journal of computer assisted radiology and surgery · 2022Article
- Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.Medical image analysis · 2021Article
- Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.Journal of medical systems · 2021Article
- Designing deep learning studies in cancer diagnostics.Nature reviews. Cancer · 2021Review
- Deep neural network models for computational histopathology: A survey.Medical image analysis · 2021Review
- Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease.Neural computing & applications · 2021Article
- Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms.Frontiers in oncology · 2021Article
- Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification.Medical & biological engineering & computing · 2021Article
- Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning.Computational and mathematical methods in medicine · 2021Article
- Artificial intelligence and machine learning in nephropathology.Kidney international · 2020Review
- Machine and deep learning methods for radiomics.Medical physics · 2020Review
- Thirdhand smoke: Genotoxicity and carcinogenic potential.Chronic diseases and translational medicine · 2020Article
- Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.Medical image analysis · 2020Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.
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