Open AccessPosted Content
Recent advances and clinical applications of deep learning in medical image analysis.
Xuxin Chen,Ximin Wang,Ke Zhang,Roy Zhang,Kar Ming Fung,Theresa C. Thai,Kathleen N. Moore,Robert S. Mannel,Hong Liu,Bin Zheng,Yuchen Qiu +10 more
TLDR
A comprehensive overview of applying deep learning methods in various medical image analysis tasks can be found in this article, where the authors highlight the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images, which are summarized based on different application scenarios.Abstract:
Deep learning has become the mainstream technology in computer vision, and it has received extensive research interest in developing new medical image processing algorithms to support disease detection and diagnosis. As compared to conventional machine learning technologies, the major advantage of deep learning is that models can automatically identify and recognize representative features through the hierarchal model architecture, while avoiding the laborious development of hand-crafted features. In this paper, we reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images, which are summarized based on different application scenarios, including lesion classification, segmentation, detection, and image registration. Additionally, we also discussed the major technical challenges and suggested the possible solutions in future research efforts.read more
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Self-supervised learning methods and applications in medical imaging analysis: A survey
Saeed Shurrab,Rehab Duwairi +1 more
TL;DR: In this article, the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis.
Journal ArticleDOI
Self-supervised learning methods and applications in medical imaging analysis: a survey
TL;DR: In this paper , the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis are reviewed.
Journal ArticleDOI
Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms
TL;DR: The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms, and achieves case classification performance with an area under ROC curve significantly outperforms state-of-the-art multi-view CNNs.
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Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism
TL;DR: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimizeDeep transfer learning models for specific application tasks can play an important role in further improving model performances.
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A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods
Gopichandh Danala,Sai Kiran Reddy Maryada,Warid Islam,Rowzat Faiz,Meredith A Jones,Yuchen Qiu,Bin Zheng +6 more
TL;DR: It is demonstrated that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.
References
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Proceedings Article
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Proceedings Article
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Proceedings ArticleDOI
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