Transfer learning techniques for medical image analysis: A review

• Autorzy: Kora Padmavathi , Ooi Chui Ping , Faust Oliver , Raghavendra U. , Gudigar Anjan , Chan Wai Yee , Meenakshi K. , Swaraja K. , Plawiak Pawel , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2021.11.004

• Języki publikacji: EN

Abstrakty

EN

Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Automated medical image analysis techniques can overcome these limitations. In this review, we investigated Transfer Learning (TL) architectures for automated medical image analysis. We discovered that TL has been applied to a wide range of medical imaging tasks, such as segmentation, object identification, disease categorization, severity grading, to name a few. We could establish that TL provides high quality decision support and requires less training data when compared to traditional deep learning methods. These advantageous properties arise from the fact that TL models have already been trained on large generic datasets and a task specific dataset is only used to customize the model. This eliminates the need to train the models from scratch. Our review shows that AlexNet, ResNet, VGGNet, and GoogleNet are the most widely used TL models for medical image analysis. We found that these models can understand medical images, and the customization refines the ability, making these TL models useful tools for medical image analysis.

Słowa kluczowe

EN

medical image; machine learning; convolutional neural network; transfer learning

PL

obrazowanie medyczne; uczenie maszynowe; sieć neuronowa konwolucyjna; transfer wiedzy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 79--107
• Opis fizyczny: Bibliogr. 265 poz., rys., tab., wykr.

Twórcy

autor

Kora Padmavathi

• Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, India

autor

Ooi Chui Ping

• School of Science and Technology, Singapore University of Social Sciences, Singapore

autor

Faust Oliver

• Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK

autor

Raghavendra U.

• Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India

autor

Gudigar Anjan

• Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

autor

Chan Wai Yee

• Department of Biomedical Imaging, Universiti Malaya Research Imaging Centre, Faculty of Medicine, Universiti Malaya, Malaysia

autor

Meenakshi K.

• Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, India

autor

Swaraja K.

• Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, India

autor

Plawiak Pawel

• Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

autor

Acharya U. Rajendra

• School of Engineering, Ngee Ann Polytechnic, Clementi, Singapore; School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan

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