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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