GroupAttention SingleShot Detector (GASSD): Finding Pulmonary Nodules in LargeScale CT Images Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D singleshot detector (SSD) called groupattention SSD (GASSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a largescale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm were annotated). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Our experimental results show that the proposed groupattention SSD outperforms the classic SSD framework as well as the stateoftheart 3DCNN, especially on some challenging lesion types.

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.