Although supervised deep convolutional neural network has shown good performance regarding lesion detection and classification using multi-parametric MRI, it is still limited by high data label requirement. In this work, we proposed a model called explanatory auxiliary generative adversarial network (ExpA-GAN), which generates heatmap for object detection under very-weak supervision (no ground truth location). The model was trained and evaluated in a public TCIA prostate dataset. Among 50 testing slices enclosing the whole prostate, the proposed model achieves 0.169 normalized distance for lesion detection, showing the potential to improve lesion detection using limited labeled data.
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