IBM Research – Almaden, San Jose, CA, USA University of Adelaide, Adelaide, SA, Australia In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. Hussein, S., Cao, K., Song, Q., Bagci, U.: Risk stratification of lung nodules using 3D CNN-based multi-task learning. 61, 663–673 (2017)ĭou, Q.: 3d deeply supervised network for automated segmentation of volumetric medical images. Shen, W., et al.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. In: IEEE International Conference on Communication Control and Computing Technologies (ICCCCT). 38(2), 915–931 (2011)Īnand, S.V.: Segmentation coupled textural feature classification for lung tumor prediction. Īrmato III, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. In: Leibe, B., Matas, J., Sebe, N., Welling, M. Kong, S., Shen, X., Lin, Z., Mech, R., Fowlkes, C.: Photo aesthetics ranking network with attributes and content adaptation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. Xie, Y., Xia, Y., Zhang, J., Feng, D.D., Fulham, M., Cai, W.: Transferable multi-model ensemble for benign-malignant lung nodule classification on chest CT. Scientific Reports 8(1), 9286 (2018)Ĭhen, S., Qin, J., Ji, X., Lei, B., Wang, T., Ni, D., Cheng, J.Z.: Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in ct images. 133–138 (2015)Ĭausey, J., et al.: Highly accurate model for prediction of lung nodule malignancy with ct scans. Kumar, D., Wong, A., Clausi, D.A.: Lung nodule classification using deep features in CT images. 67(1), 7–30 (2018)ĭel Ciello, A., Franchi, P., Contegiacomo, A., Cicchetti, G., Bonomo, L., Larici, A.R.: Missed lung cancer: when, where, and why? Diagn. Siegel, R.L., Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. Extensive experiments demonstrated that our approach achieved competitive classification performance and more accurate attribute scoring over the state-of-the-arts. We validated the efficacy of our MTMR-Net on the public benchmark LIDC-IDRI dataset. Furthermore, a siamese network with a novel margin ranking loss was elaborately designed to enhance the discrimination capability on ambiguous nodule cases. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. The relatedness between lung nodule classification and attribute score regression is explicitly explored in our multi-task model, which can contribute to the performance gains of both tasks. To meet these challenges, we propose a Multi- Task deep learning framework with a novel Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. However, this is quite challenging due to the considerable difficulty of nodule heterogeneity modelling and limited discrimination capability on ambiguous cases. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score grading. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Lung cancer is the leading cause of cancer deaths worldwide.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |