Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
This work addresses data scarcity in medical image analysis, specifically for liver lesion classification, and is incremental as it builds on existing image generation methods.
The paper tackled the problem of limited training data for liver lesion classification in CT by generating synthetic data through mixing class-specified and unspecified representations, resulting in a 7.4% average accuracy improvement over the baseline.
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme.