Ece Kendir Cakmak, Aysenur Ugurlu, Berk Anbaroglu
Adopting citizen science approach for water quality monitoring in Uzungöl, Turkey
Environmental monitoring and assessment
two major challenges deeplearningbased medical image segmentation multimodality lack expert annotations existing semisupervised segmentation models mitigate problem insufficient annotations utilizing small amount labeled data however models limited singlemodal data cannot exploit complementary information multimodal medical images semisupervised multimodal models proposed recently rigid structures require additional training steps modality work propose novel flexible method semisupervised multimodal medical image segmentation unified translation smsut unique semisupervised procedure leverage multimodal information improve semisupervised segmentation performance architecture capitalizes unified translation extract complementary information multimodal data compels network focus disparities salient features among modality furthermore impose constraints model pixel feature levels cope lack annotation information diverse representations within semisupervised multimodal data introduce novel training procedure tailored semisupervised multimodal medical image analysis integrating concept conditional translation method remarkable ability seamless adaptation varying numbers distinct modalities training data experiments show model exceeds semisupervised segmentation counterparts public datasets proves networks highperformance capabilities transferability proposed method code method openly available httpsgithubcomsue1347smsutmedicalimgsegmentation