PDF(Pubmed)
Abstract：
UNASSIGNED: Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs.
UNASSIGNED: In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients\' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing.
UNASSIGNED: The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a p -value of 0.0054 for precision and a p -value of 0.00034 for specificity.
UNASSIGNED: In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.
UNASSIGNED: In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients\' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing.
UNASSIGNED: The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a p -value of 0.0054 for precision and a p -value of 0.00034 for specificity.
UNASSIGNED: In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.
摘要：
肺癌是全球第二常见的癌症，也是导致癌症死亡的主要原因。低剂量计算机断层扫描（LDCT）是推荐的早期发现肺癌的影像学筛查工具。一种完全自动化的LDCT计算机辅助检测方法将极大地改善现有的临床工作流程。大多数现有的肺部检测方法都是为高剂量CT（HDCT）设计的，由于域移位和LDCT图像质量差，这些方法不能直接应用于LDCT。在这项工作中,我们描述了一种基于半自动化迁移学习的方法,用于使用LDCT早期检测肺结节.
■在这项工作中，我们开发了一种基于目标检测模型的算法，你只看一次(YOLO)来检测肺结节。YOLO模型首先是在CT上训练的，并且在使用医学到医学转移学习方法在LDCT上重新训练模型期间使用预训练权重作为初始权重。这项研究的数据集是来自一项筛选试验，该试验由从连续3年（T1，T2和T3）获得的50名活检证实的肺癌患者获得的LDCT组成。大约60名肺癌患者的HDCT来自一个公共数据集。使用包含15例患者病例（93个具有癌结节的切片）的固定测试集对开发的模型进行了评估，特异性，召回，和F1得分。评估指标每年按患者报告，并平均3年。为了进行比较分析，所提出的检测模型使用COCO数据集的预训练权重作为初始权重进行训练.采用α值为0.05的配对t检验和卡方检验进行统计学显著性检验。
■通过比较使用HDCT预训练权重与COCO预训练权重开发的拟议模型来报告结果。前一种方法与后一种方法在检测癌结节方面获得了0.982对0.93的精度，0.923与0.849的特异性在识别无癌结节的切片，召回率分别为0.87和0.886，F1评分为0.924和0.903。随着结节的进展，前一种方法的精密度为1,特异性为0.92,灵敏度为0.930.在对比研究中进行的统计分析导致精确度的p值为0.0054,特异性的p值为0.00034。
■在这项研究中，开发了一种半自动方法来检测LDCT中的肺结节,使用HDCT预训练权重作为初始权重并重新训练模型.Further,通过将上述方法中的HDCT预训练权重替换为COCO预训练权重来比较结果.所提出的方法可以在筛查程序中识别早期肺结节，减少由于LDCT误诊而导致的过度诊断和随访，在受影响的患者中开始治疗方案，降低死亡率。
■在这项工作中，我们开发了一种基于目标检测模型的算法，你只看一次(YOLO)来检测肺结节。YOLO模型首先是在CT上训练的，并且在使用医学到医学转移学习方法在LDCT上重新训练模型期间使用预训练权重作为初始权重。这项研究的数据集是来自一项筛选试验，该试验由从连续3年（T1，T2和T3）获得的50名活检证实的肺癌患者获得的LDCT组成。大约60名肺癌患者的HDCT来自一个公共数据集。使用包含15例患者病例（93个具有癌结节的切片）的固定测试集对开发的模型进行了评估，特异性，召回，和F1得分。评估指标每年按患者报告，并平均3年。为了进行比较分析，所提出的检测模型使用COCO数据集的预训练权重作为初始权重进行训练.采用α值为0.05的配对t检验和卡方检验进行统计学显著性检验。
■通过比较使用HDCT预训练权重与COCO预训练权重开发的拟议模型来报告结果。前一种方法与后一种方法在检测癌结节方面获得了0.982对0.93的精度，0.923与0.849的特异性在识别无癌结节的切片，召回率分别为0.87和0.886，F1评分为0.924和0.903。随着结节的进展，前一种方法的精密度为1,特异性为0.92,灵敏度为0.930.在对比研究中进行的统计分析导致精确度的p值为0.0054,特异性的p值为0.00034。
■在这项研究中，开发了一种半自动方法来检测LDCT中的肺结节,使用HDCT预训练权重作为初始权重并重新训练模型.Further,通过将上述方法中的HDCT预训练权重替换为COCO预训练权重来比较结果.所提出的方法可以在筛查程序中识别早期肺结节，减少由于LDCT误诊而导致的过度诊断和随访，在受影响的患者中开始治疗方案，降低死亡率。
