Robust 3D Convolutional Neural Networks For Pulmonary Nodule Detection Computeraided Diagnosis(CADx) plays an important role in interpretation of medical images. In various kinds of medical images, Computed Tomography(CT) images are able to provide pivotal evidence for diagnosis of lung cancer. Especially, Earlystage lung cancer can be discovered by detecting small pulmonary nodules in patientxe2x80x99s CT slices. Thus, it is worthwhile to develop pulmonary nodules detection algorithm. In this paper, we introduce a pulmonary nodules detection algorithm based on object detection algorithm, with three improved techniques that focus on characteristics of pulmonary nodules detection algorithm. First, we use 3Dconvolutional layers to process CT images instead of 2Dconvolutional layers. Second, We designed a powerful encoder to extract image features, which can train with a small batch for active learning or retraining by hospital with limited computing resources. Third, we propose filterRPN for filtering a large number of invalid anchors and accelerate the training process. Finally, we use some experiments to show that our model got the startoftheart results.

Automatic Computer Aided System for Lung Cancer in Chest CTs Using MDRFCN Combined with TriLevel Region Proposal Network Pulmonary cancer is one of the major causes of deaths caused by cancer around the globe. Early stage lung cancer detection can prove to be essential for the patients, for which the computed tomography (CT) images are analyzed by the radiologists to determine the presence of nodules and diagnose the disease. Conventional techniques used by the radiologists for nodule detection in CT images is timeconsuming and inefficient; to assist in the diagnosis process and further enhance its efficiency and accuracy, decision support systems have been developed in the past few years. In our paper, we proposed a MultiDimension Regionbased Fully Convolutional Network based decision support system for detection and classification of lung nodule. The MultiDimension RFCN serves as an image classifier backbone for our feature extraction step in addition to the proposed TriLevel Region Proposal Network (3LRPN) along with the positionsensitive score maps (PSSM) being explored. A novel median intensity projection method is used to leverage the multidimensional information from CT images and introduced an additional deconvolutional layer to adopt the proposed TriLevel Region Proposal Network in our architecture to automatically identify the potential Region of Interest. We trained and evaluated our proposed decision support system using LIDCIDRI dataset. The evaluation results demonstrated the high level performance of our proposed model in comparison to the stateoftheart nodule detection and classification methods by attaining classification accuracy of 97.61% and sensitivity of 97.4%.