A Transfer Learning Based Active Learning Framework for Brain Tumor Classification

• URL: http://arxiv.org/abs/2011.09265v1
• Date: Mon, 16 Nov 2020 21:11:40 GMT
• Title: A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
• Authors: Ruqian Hao, Khashayar Namdar, Lin Liu, Farzad Khalvati
• Abstract summary: We propose a novel transfer learning based active learning framework to reduce the annotation cost. We employ a 2D slice-based approach to train and finetune our model on the Magnetic Resonance Imaging (MRI) training dataset of 203 patients. With our proposed method, the model achieved Area Under Receiver Operating Characteristic (ROC) Curve (AUC) of 82.89% on a separate test dataset of 66 patients.
• Score: 10.437969366798411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, Artificial Intelligence-enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images given the complexity and volume of medical data. In this work, we propose a novel transfer learning based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. We employed a 2D slice-based approach to train and finetune our model on the Magnetic Resonance Imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved Area Under Receiver Operating Characteristic (ROC) Curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

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