Lung Nodule4 Armato, Samuel et al., 2011, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans # Summary #1. Purpose: To develop computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment, they established the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) to complete the database.2. Methods: The LIDC/IDRI Database contains 1018 cases, each consisting of images from a clinical thoracic CT scan and an a.. 2025. 2. 27. Ardila et al., 2019, End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography # 세줄요약 # We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies: Fi.. 2019. 11. 11. Liao et al., 2017, Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network # 세줄요약 # Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. The model consists of two modules, the first one is a 3D region proposal network for nodule detection, which outputs all suspicious nodules for a subject, then the second one selects the top five nodu.. 2019. 11. 11. Nasrullah et al., 2019, Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies # 세줄요약 # 높은 사망률을 가지는 폐암을 조기 진단하기 위하여 폐 CT 영상에서 딥러닝을 이용한 기존의 많은 폐결절(Lung Nodule) 진단 알고리즘이 연구되어 왔으나, 거짓 양성(False Positive)를 구분해내는 것이 어려워 이 논문에서는 환자들의 진단 정보(Clinical Information)를 딥러닝 학습에 함께 사용하였다. 따라서 폐 결절 진단 알고리즘은 여러 단계로 구성되며, 폐 CT 영상을 이용한 영상 딥러닝에서는 CMixNet(Customized mixed link Network)을 기반으로 faster R-CNN을 이용한 결절 포착(Nodule Detection) 단계와 GBM(Gradient Boosting Machine)을 이용한 결절 분류(Nodule Classific.. 2019. 11. 11. 이전 1 다음
