Medical image grading learning based on active and incremental fine-tuning

Zhuo Su; Jiwei Hu; Quan Liu; Jiamei Deng

doi:10.1117/12.2540469

14 August 2019 Medical image grading learning based on active and incremental fine-tuning

Zhuo Su, Jiwei Hu, Quan Liu, Jiamei Deng

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117916 (2019) https://doi.org/10.1117/12.2540469
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

The development of convolutional neural network has brought great achievements to image classification in recent years. However, the classification performance is good only for natural images rather than medical images. An important reason is that the medical image database used for training is always deficient. So how to use these limited data to acquire more extensive features has become a hot research focus. In this paper, we first update the order and number of the whole training data every time in active and incremental fine-tuning. Then we set different contribution rate for the data selected in our model, which based on the information quantity of the data in training stage and make our model converge steadily. After that, a pre-trained model and our preprocessed datasets are employed, which allows us to further fine-tune our models. The experiments evaluated on two different biomedical datasets shows that our model can achieve promising results.

Citation Download Citation

Zhuo Su, Jiwei Hu, Quan Liu, and Jiamei Deng "Medical image grading learning based on active and incremental fine-tuning", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117916 (14 August 2019); https://doi.org/10.1117/12.2540469

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KEYWORDS

Data modeling

Medical imaging

Breast cancer

Visual process modeling

Biomedical optics

Convolutional neural networks

Performance modeling

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