Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations.
Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations.
复制标题
DOI：
10.1016/j.artmed.2021.102078
复制
发表时间：
2021-06
影响因子：
7.5
通讯作者：
Gholipour A
中科院分区：
文献类型：
--
作者：
Karimi D;Warfield SK;Gholipour A
我们对迁移学习在训练用于医学图像分割的全卷积网络（FCN）中的作用进行了严格评估。我们首先表明，虽然迁移学习减少了目标任务的训练时间，但分割准确性的提高高度依赖于任务/数据。只有当分割任务更具挑战性并且目标训练数据较小时，才能观察到较大的改进。我们通过研究迁移学习对模型参数和学习表示的演变的影响来阐明这些观察结果。我们观察到卷积滤波器在训练过程中变化很小，并且在收敛时仍然看起来很随机。我们进一步证明，通过将网络的编码器部分冻结在随机值上，只训练解码器部分，可以构建非常准确的FCN。至少对于医学图像分割，这一发现挑战了编码器部分需要学习数据/任务特定表示的普遍信念。我们研究了FCN表示的演变，以更深入地了解迁移学习对训练动态的影响。我们的分析表明，尽管通过迁移学习训练的FCN学习到的表示与随机初始化训练的FCN不同，但通过迁移学习训练的FCN之间的变异性可能与随机初始化训练的FCN之间的变异性一样高。此外，特征重用并不局限于早期的编码器层;相反，它在更深的层中可能更重要。这些发现提供了新的见解，并提出了训练用于医学图像分割的FCN的替代方法。
We present a critical assessment of the role of transfer learning in training fully convolutional networks (FCNs) for medical image segmentation. We first show that although transfer learning reduces the training time on the target task, improvements in segmentation accuracy are highly task/data-dependent. Large improvements are observed only when the segmentation task is more challenging and the target training data is smaller. We shed light on these observations by investigating the impact of transfer learning on the evolution of model parameters and learned representations. We observe that convolutional filters change little during training and still look random at convergence. We further show that quite accurate FCNs can be built by freezing the encoder section of the network at random values and only training the decoder section. At least for medical image segmentation, this finding challenges the common belief that the encoder section needs to learn data/task-specific representations. We examine the evolution of FCN representations to gain a deeper insight into the effects of transfer learning on the training dynamics. Our analysis shows that although FCNs trained via transfer learning learn different representations than FCNs trained with random initialization, the variability among FCNs trained via transfer learning can be as high as that among FCNs trained with random initialization. Moreover, feature reuse is not restricted to the early encoder layers; rather, it can be more significant in deeper layers. These findings offer new insights and suggest alternative ways of training FCNs for medical image segmentation.
登录
查看更多内容
DOI：
10.1007/s11548-018-1785-8
发表时间：
2018-08-01
影响因子：
3
作者：
Karimi, Davood;Samei, Golnoosh;Salcudean, Septimiu E.
通讯作者：
Salcudean, Septimiu E.
影响因子：
10.9
作者：
Cheplygina, Veronika;de Bruijne, Marleen;Pluim, Josien P. W.
通讯作者：
Pluim, Josien P. W.
影响因子：
5.7
作者：
Bastiani M;Andersson JLR;Cordero-Grande L;Murgasova M;Hutter J;Price AN;Makropoulos A;Fitzgibbon SP;Hughes E;Rueckert D;Victor S;Rutherford M;Edwards AD;Smith SM;Tournier JD;Hajnal JV;Jbabdi S;Sotiropoulos SN
通讯作者：
Sotiropoulos SN
影响因子：
10.6
作者：
Heimann, Tobias;van Ginneken, Bram;Wolf, Ivo
通讯作者：
Wolf, Ivo
影响因子：
10.6
作者：
Bernard, Olivier;Lalande, Alain;Jodoin, Pierre-Marc
通讯作者：
Jodoin, Pierre-Marc