An adversarial multi-task training objective that separates task-specific features improves standard-plane classification in fetal ultrasound when previously unseen shadow artifacts appear.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
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abstract
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.
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cs.LG 1years
2019 1verdicts
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Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
An adversarial multi-task training objective that separates task-specific features improves standard-plane classification in fetal ultrasound when previously unseen shadow artifacts appear.