Focusnet++: Attentive aggregated transformations for efficient and accurate medical image segmentation
Proceedings - International Symposium on Biomedical Imaging
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms with group convolutions to create our group attention mechanism, which forms the fundamental building block of our network, FocusNet++. We employ a hybrid loss based on balanced cross entropy, Tversky loss and the adaptive logarithmic loss to enhance the performance along with fast convergence. Our results show that FocusNet++ achieves state-of-the-art results across various benchmark metrics for the ISIC 2018 melanoma segmentation and the cell nuclei segmentation datasets with fewer parameters and FLOPs.
Group Attention, Medical Image Segmentation, Residual Learning
C. Kaul, N. Pears, H. Dai, R. Murray-Smith and S. Manandhar, "Focusnet++: Attentive Aggregated Transformations For Efficient And Accurate Medical Image Segmentation," in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021, pp. 1042-1046, doi: 10.1109/ISBI48211.2021.9433918.