The medical imaging analysis project is mainly supported by the FELIX project, a long-term funding provided by the Lustgarten foundation aimed at detecting the pancreas neoplasm using deep learning techniques. The main story is to ask the professional radiologists to annotate medical data (such as CT scans), and train deep networks to learn from these knowledge. In the first year, we are mainly working on segmenting normal pancreases from abdominal CT scans, and we have achieved the state-of-the-art accuracy [1,3,4] in a public dataset. Also we had some preliminary studies in detecting pancreatic cysts . In the current (second) year, we move on to deal with abnormal pancreases, in particular the most common pancreatic cancer known as pancreatic ductal adenocarcinoma (PDAC). In the fundamental research, we are also interested in the advantages and disadvantages of 2D and 3D segmentation approaches.
 Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille, Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation, arXiv preprint: arXiv 1709.04518, 2017 (submitted to CVPR 2018).
 Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille, Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans, MICCAI 2017.
 Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, Elliot K. Fishman, Alan L. Yuille, A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans, MICCAI 2017 (project page).
 Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille, A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation, arXiv preprint: arXiv 1712.00201, 2017 (submitted to CVPR 2018).