  To better retain the deep features of an image and solve the sparsity problem
of the end-to-end segmentation model, we propose a new deep convolutional
network model for medical image pixel segmentation, called MC-Net. The core of
this network model consists of four parts, namely, an encoder network, a
multiple max-pooling integration module, a cross multiscale deconvolution
decoder network and a pixel-level classification layer. In the network
structure of the encoder, we use multiscale convolution instead of the
traditional single-channel convolution. The multiple max-pooling integration
module first integrates the output features of each submodule of the encoder
network and reduces the number of parameters by convolution using a kernel size
of 1. At the same time, each max-pooling layer (the pooling size of each layer
is different) is spliced after each convolution to achieve the translation
invariance of the feature maps of each submodule. We use the output feature
maps from the multiple max-pooling integration module as the input of the
decoder network; the multiscale convolution of each submodule in the decoder
network is cross-fused with the feature maps generated by the corresponding
multiscale convolution in the encoder network. Using the above feature map
processing methods solves the sparsity problem after the max-pooling
layer-generating matrix and enhances the robustness of the classification. We
compare our proposed model with the well-known Fully Convolutional Networks for
Semantic Segmentation (FCNs), DecovNet, PSPNet, U-net, SgeNet and other
state-of-the-art segmentation networks such as HyperDenseNet, MS-Dual,
Espnetv2, Denseaspp using one binary Kaggle 2018 data science bowl dataset and
two multiclass dataset and obtain encouraging experimental results.
