1 research outputs found
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Increased information sharing through short and long-range skip connections
between layers in fully convolutional networks have demonstrated significant
improvement in performance for semantic segmentation. In this paper, we propose
Competitive Dense Fully Convolutional Networks (CDFNet) by introducing
competitive maxout activations in place of naive feature concatenation for
inducing competition amongst layers. Within CDFNet, we propose two
architectural contributions, namely competitive dense block (CDB) and
competitive unpooling block (CUB) to induce competition at local and global
scales for short and long-range skip connections respectively. This extension
is demonstrated to boost learning of specialized sub-networks targeted at
segmenting specific anatomies, which in turn eases the training of complex
tasks. We present the proof-of-concept on the challenging task of whole body
segmentation in the publicly available VISCERAL benchmark and demonstrate
improved performance over multiple learning and registration based
state-of-the-art methods.Comment: Paper accepted on MICCAI-MLMI 2018 worksho