FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation

Chaitanya Kaul, N. E. Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Publication statusPublished - 25 May 2021
EventIEEE International Symposium on Biomedical Imaging, 2021: ISBI 2021 - Online, Nice, France
Duration: 13 Apr 202116 Apr 2021


ConferenceIEEE International Symposium on Biomedical Imaging, 2021
Abbreviated titleISBI 2021
Internet address


  • Group Ateention, Medical Image Segmentation, Residual Learning

Cite this