Noura El Salibi, Sawsan Abdulrahim, Maria El Haddad, Berthe Abi Zeid, Marwan F Alawieh, Zeinab Ramadan, Hala Ghattas, Stephen J McCall
Predicting intention to vaccinate against COVID-19 in older Syrian refugees in Lebanon: Findings from a multi-wave study
Vaccine
accurate volumetric segmentation brain tumors tissues beneficial quantitative brain analysis brain disease identification multimodal magnetic resonance mr images nevertheless due complex relationship modalities 3d fully convolutional networks 3d fcns using simple multimodal fusion strategies hardly learn complex nonlinear complementary information modalities meanwhile indiscriminative feature aggregation lowlevel highlevel features easily causes volumetric feature misalignment 3d fcns hand 3d convolution operations 3d fcns excellent modeling local relations typically inefficient capturing global relations distant regions volumetric images tackle issues propose aligned crossmodality interaction network acminet segmenting regions brain tumors tissues mr images network crossmodality feature interaction module first designed adaptively efficiently fuse refine multimodal features secondly volumetric feature alignment module developed dynamically aligning lowlevel highlevel features learnable volumetric feature deformation field thirdly propose volumetric dual interaction graph reasoning module graphbased global context modeling spatial channel dimensions proposed method applied brain glioma vestibular schwannoma brain tissue segmentation tasks performed extensive experiments brats2018 brats2020 vestibular schwannoma iseg2017 datasets experimental results show acminet achieves stateoftheart segmentation performance four benchmark datasets obtains highest dsc score hardsegmented enhanced tumor region validation leaderboard brats2020 challenge