Abstract
Bone scintigraphy is widely used to diagnose tumor metastases. It is of great importance to accurately locate and segment hotspots from bone scintigraphy. Previous computer-aided diagnosis methods mainly focus on locating abnormalities instead of accurately segmenting them. In this paper, we propose a new framework that accomplish the two tasks at the same time. We first use sparse autoencoder and convolution neural network (CNN) to train an image-level classifier that label input image as normal or suspected. For suspected images, multiple instance learning (MIL) is applied to train a patch-level classifier. Then we use this classifier to produce a probability map of hotspots. Finally, level set segmentation is performed with the probability map as initial condition. The experimental results demonstrate that our method is more accurate and robust than other methods.
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This research is partly supported by NSFC, China (No: 61375048).
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Geng, S., Jia, S., Qiao, Y., Yang, J., Jia, Z. (2015). Combining CNN and MIL to Assist Hotspot Segmentation in Bone Scintigraphy. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_53
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DOI: https://doi.org/10.1007/978-3-319-26561-2_53
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