Gliomas are the most common and aggressive among brain tumors, which cause a
short life expectancy in their highest grade. Therefore, treatment assessment
is a key stage to enhance the quality of the patients' lives. Recently, deep
convolutional neural networks (DCNNs) have achieved a remarkable performance in
brain tumor segmentation, but this task is still difficult owing to high
varying intensity and appearance of gliomas. Most of the existing methods,
especially UNet-based networks, integrate low-level and high-level features in
a naive way, which may result in confusion for the model. Moreover, most
approaches employ 3D architectures to benefit from 3D contextual information of
input images. These architectures contain more parameters and computational
complexity than 2D architectures. On the other hand, using 2D models causes not
to benefit from 3D contextual information of input images. In order to address
the mentioned issues, we design a low-parameter network based on 2D UNet in
which we employ two techniques. The first technique is an attention mechanism,
which is adopted after concatenation of low-level and high-level features. This
technique prevents confusion for the model by weighting each of the channels
adaptively. The second technique is the Multi-View Fusion. By adopting this
technique, we can benefit from 3D contextual information of input images
despite using a 2D model. Experimental results demonstrate that our method
performs favorably against 2017 and 2018 state-of-the-art methods.Comment: 7 pages, 5 figures, 4 tables, Accepted by ICCKE 201