Fine-tuning convolutional deep features for MRI based brain tumor classification

Kaoutar B. Ahmed; Lawrence O. Hall; Dmitry B. Goldgof; Renhao Liu; Robert A. Gatenby

doi:10.1117/12.2253982

3 March 2017 Fine-tuning convolutional deep features for MRI based brain tumor classification

Kaoutar B. Ahmed, Lawrence O. Hall, Dmitry B. Goldgof, Renhao Liu, Robert A. Gatenby

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342E (2017) https://doi.org/10.1117/12.2253982
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient’s prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN’s, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

Citation Download Citation

Kaoutar B. Ahmed, Lawrence O. Hall, Dmitry B. Goldgof, Renhao Liu, and Robert A. Gatenby "Fine-tuning convolutional deep features for MRI based brain tumor classification", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342E (3 March 2017); https://doi.org/10.1117/12.2253982

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