PocketNet: A Smaller Neural Network for Medical Image Analysis
PocketNet: A Smaller Neural Network for Medical Image Analysis
复制标题
DOI：
10.1109/tmi.2022.3224873
复制
发表时间：
2021-04
影响因子：
10.6
通讯作者：
A. Celaya;Jonas A. Actor;Rajarajesawari Muthusivarajan;Evan Gates;C. Chung;D. Schellingerhout;B. Rivière;David T. Fuentes
中科院分区：
文献类型：
--
作者：
A. Celaya;Jonas A. Actor;Rajarajesawari Muthusivarajan;Evan Gates;C. Chung;D. Schellingerhout;B. Rivière;David T. Fuentes
医学成像深度学习模型往往又大又复杂，需要专门的硬件来训练和评估这些模型。为了解决这些问题，我们提出了Pocketnet范式，通过抑制卷积神经网络中通道数量的增长来减少深度学习模型的规模。我们证明，对于一系列分割和分类任务，Pocketnet架构产生的结果与传统神经网络相当，同时将参数数量减少了数个数量级，使用的GPU内存减少了90%，训练时间加快了40%，从而允许在资源受限的环境中训练和部署此类模型。
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings.