Shadow Detection for Ultrasound Images Using Unlabeled Data and Synthetic Shadows
By Suguru Yasutomi, T. Arakaki, Ryuji Hamamoto. 2019
14 citations recorded by the source corpus.
Medical ultrasound is widely used technique for diagnosing internal organs. As common artifacts, shadows often appear in ultrasound images. Detecting such shadows is curious because they prevent accurate diagnosis. In this paper, we propose a novel shadow detection method based on auto-encoding structure. It once separates an input image into a shadow image and a content image using two decoders and combines them to reconstruct the input. To lead the network into separating the input, we inject synthetic shadows into the input and make the network to predict them as the shadow image. Since we know the rough shape of shadows as basic domain knowledge, we can generate plausible shadows. These processes are achieved by using only unlabeled data. Experiments on ultrasound images for fetal heart diagnosis shows the effectiveness of the method.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning (2020, background): “In recent years, with the progress of machine learning technology mainly on deep learning, the expectations of artificial intelligence has been increasing, and research on its medical application has been actively progressing [9–12].”