**5. Conclusions**

Although the number of training images in our IHC dataset was small, it is observed from the obtained results in these experiments that the proposed detector has significantly achieved promising results in semantic segmentation. This is due to its architecture, which employs the following; firstly, it uses atrous/dilated convolution layers as a way to widen the field of view over the input feature maps without increasing the number of parameters. It also uses the ASPP module to deal with the different scales problem of objects in the image. Furthermore, It uses an encoderdecoder architecture. Hence it reduces the resulting output dimensions through passing multiple convolution layers with strides of 1 or more to avoid pooling layers in the network. Finally, it passes the output through a decoder with learnable parameters to regain the original dimensions. For this reason, we have chosen this architecture for the proposed detector to segment the cellular membranes of colorectal IHC images semantically.

In conclusion, we have presented an end-to-end trainable deep neural network to tackle the problem of cellular membranes in colorectal IHC images. The proposed architecture has achieved a good performance compared with other methods. Hence, the proposed detector is able to objectively and automatically detect glands, thus easing the burden of pathologists.
