Abstract
Classification of pancreatic cystic neoplasms (PCN) into subclasses is crucial since their treatments are different. However, accurate classification is very difficult even for radiologists, due to similar appearance and shape. We propose a network called PCN-Net which makes use of T1/T2 MRI of abdomen by its three stages design. The first and second stages are trained on T1 and T2 separately for detection and inter-modality registration. After a Z-Continuity Filter and modalities fusion, the third stage predict the results with registered image pairs. On a database of 48 patients, our method can predict with slice level accuracy of \(80.0\%\) and patient level accuracy of \(92.3\%\), which are much better than other baseline methods.
W. Chen and H. Ji—contributed equally to this work.
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This work is supported by the National Natural Science Foundation of China under Grant 61622207.
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Chen, W. et al. (2018). Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_19
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