Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation

Abstract

The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of patients who have acquired the condition to be diagnosed. Second, the training data may be corrupted by various types of noise. Here, we study the problem of brain tumor detection from magnetic resonance spectroscopy (MRS) data, where both types of problems are prominent. To overcome these challenges, we propose a new method for training a deep neural network that distills particularly representative training examples and augments the training data by mixing these samples from one class with those from the same and other classes to create additional training samples. We demonstrate that this technique substantially improves performance, allowing our method to reach human-expert-level accuracy with just a few thousand training examples. Interestingly, the network learns to rely on features of the data that are usually ignored by human experts, suggesting new directions for future research.

Submitted to IEEE Transactions on Neural Networks and Learning Systems

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SMOTE: Synthetic Minority Over-sampling Technique
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• Understanding Neural Networks Through Deep Visualization
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Deep Learning is Robust to Massive Label Noise
• Data Augmentation by Pairing Samples for Images Classification
• Learning how to explain neural networks: PatternNet and PatternAttribution
• Learning From Noisy Large-Scale Datasets With Minimal Supervision