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  1. NLST Data Requests
  2. NDR-137

NLST-403 Request for Path Images

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      Principal Investigator
      Name
      Ming Li
      Degrees
      Ph.D
      Institution
      Huadong hospital
      Position Title
      professor
      Email
      minli77@163.com
      Project Information
      Study
      NLST
      Project ID
      NLST-403
      Title
      Predicting Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas from CT Scan with 3D Convolutional Neural Networks
      Summary
      The system processes a 3D patch of raw CT, and learns a deep representation from a given nodule. A dataset of more than 500 nodules was used in this paper. We train and validate our deep learning system on 4/5 nodules, and test the performance on 1/5 nodules. An independent public dataset is designed to refine our model.

      Aims
      In this project, we aimed to develop a deep learning system based on 3D convolutional neural networks, which automatically predicts the tumor invasiveness and to build a deep learning model which could help doctors working efficiently and facilitate the precision medicine.

      Collaborators
      Jiancheng Yang Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240 P.R. China, Diannei Technology, Shanghai 200050 P.R. China
      Peijun Wang Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China

            tracyn T Nolan
            tracyn T Nolan
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