基于表示学习和神经形态计算的深度医学图像分析
Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
摘要：
我们探讨了三个有代表性的研究路线，并展示了我们的方法在脑癌MRI数据分类基准上的实用性。首先，我们提出了一个胶囊网络，它显式地学习对旋转和仿射变换鲁棒的表示。该模型需要更少的训练数据，并且优于原始卷积基线和之前的胶囊网络实现。其次，我们利用最新的域自适应技术来实现新的最先进的准确性。我们的实验表明，非医学图像可以用来提高模型的性能。最后，我们设计了一个在英特尔Loihi神经形态芯片上训练的尖峰神经网络（图1显示了推理快照）。该模型消耗低得多的功率，同时在给定模型简化的情况下实现合理的精度。我们认为，在这个方向上结合硬件和学习进步的更多研究将为未来的医学成像（设备上的AI，Few-Shot预测，自适应扫描）提供动力。
英文摘要：
We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data. First, we present a capsule network that explicitly learns a representation robust to rotation and affine transformation. This model requires less training data and outperforms both the original convolutional baseline and a previous capsule network implementation. Second, we leverage the latest domain adaptation techniques to achieve a new state-of-the-art accuracy. Our experiments show that non-medical images can be used to improve model performance. Finally, we design a spiking neural network trained on the Intel Loihi neuromorphic chip (Fig. 1 shows an inference snapshot). This model consumes much lower power while achieving reasonable accuracy given model reduction. We posit that more research in this direction combining hardware and learning advancements will power future medical imaging (on-device AI, few-shot prediction, adaptive scanning).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
