计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211200097-6.doi: 10.11896/jsjkx.211200097
徐坤财1, 冯宝2, 陈业航2, 刘昱2, 周皓阳2, 陈相猛3
XU Kun-cai1, FENG Bao2, CHEN Ye-hang2, LIU Yu2, ZHOU Hao-yang2, CHEN Xiang-meng3
摘要： 针对胸腺瘤患者术前危险程度的预测问题,提出了结合深度学习与改进的极限学习机的集成学习计算机辅助分析方法。首先,将胸腺瘤CT图像通过小波多尺度变换到不同的尺度下并计算小波能量图,以增加图像信息的丰富性和多样性;其次,利用小波能量图训练卷积神经网络模型,并利用卷积核提取小波能量图中与任务相关的特异性深度特征;最后,基于改进的极限学习机为基分类器训练具有差异性的子模型并构建集成学习分类模型,以提高模型的稳定性和预测精度。多中心实验结果表明,所提方法有较好的泛化性能和稳定性,3个验证集的AUC分别为0.833,0.771,0.784。
中图分类号:
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