In this paper, we present a fully automatic brain tumor segmentation and
classification model using a Deep Convolutional Neural Network that includes a
multiscale approach. One of the differences of our proposal with respect to
previous works is that input images are processed in three spatial scales along
different processing pathways. This mechanism is inspired in the inherent
operation of the Human Visual System. The proposed neural model can analyze MRI
images containing three types of tumors: meningioma, glioma, and pituitary
tumor, over sagittal, coronal, and axial views and does not need preprocessing
of input images to remove skull or vertebral column parts in advance. The
performance of our method on a publicly available MRI image dataset of 3064
slices from 233 patients is compared with previously classical machine learning
and deep learning published methods. In the comparison, our method remarkably
obtained a tumor classification accuracy of 0.973, higher than the other
approaches using the same database.

本文提出了一种完全自动化的使用深度卷积神经网络的脑肿瘤分割和分类模型，包括多尺度方法。与以前的工作相比，我们的提案的一个区别是，输入图像在不同的处理路径上以三个空间尺度进行处理。这个机制受到了人类视觉系统的内在操作的启发。所提出的神经模型可以分析包含三种类型肿瘤的 MRI 图像：脑膜瘤、胶质瘤和垂体瘤，并且不需要预处理输入图像以提前去除颅骨或椎骨部分。我们的方法在一个包含 233 名患者 3064 张切片的公开可用的 MRI 图像数据集上的性能与以前的经典机器学习和深度学习方法进行了比较。在比较中，我们的方法以 0.973 的肿瘤分类准确度明显高于使用相同数据库的其他方法。

使用多尺度卷积神经网络的深度学习方法进行脑肿瘤分类和分割

A Deep Learning Approach for Brain Tumor Classification and Segmentation  Using a Multiscale Convolutional Neural Network

Learning both hierarchical and temporal representation has been among the
long-standing challenges of recurrent neural networks. Multiscale recurrent
neural networks have been considered as a promising approach to resolve this
issue, yet there has been a lack of empirical evidence showing that this type
of models can actually capture the temporal dependencies by discovering the
latent hierarchical structure of the sequence. In this paper, we propose a
novel multiscale approach, called the hierarchical multiscale recurrent neural
networks, which can capture the latent hierarchical structure in the sequence
by encoding the temporal dependencies with different timescales using a novel
update mechanism. We show some evidence that our proposed multiscale
architecture can discover underlying hierarchical structure in the sequences
without using explicit boundary information. We evaluate our proposed model on
character-level language modelling and handwriting sequence modelling.

本文提出了一种多尺度递归神经网络，称为分层多尺度递归神经网络，通过使用新的更新机制，在不使用显式边界信息的情况下捕获序列中的潜在分层结构，从而解决递归神经网络中的层次和时间表示问题，并在字符级别语言建模和手写序列建模上进行评估。