Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling
Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain. During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance. However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples. In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation. To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process. To evaluate the proposed idea, we perform an experiment on a medical image dataset IVUS for biophysical simulation task.
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