论文标题
深度医学图像分析，具有代表性学习和神经形态计算
Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
论文作者
论文摘要
我们探讨了三个代表性的研究线，并证明了我们在脑癌MRI数据的分类基准上的方法的实用性。首先，我们提出一个胶囊网络，该网络明确地学习了对旋转和仿射变换的强大表示。该模型需要更少的培训数据，并且胜过原始卷积基线和以前的胶囊网络实现。其次，我们利用最新的域适应技术来实现新的最新精度。我们的实验表明，非医学图像可用于改善模型性能。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）。在降低模型的情况下，该模型消耗了较低的功率，同时实现了合理的精度。我们认为，将硬件和学习进步结合起来的更多研究将为未来的医学成像提供动力（eviCe ai，几乎没有预测，自适应扫描）。
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).
