Racheal Alinaitwe, Seggane Musisi, David Mukunya, Yvette Wibabara, Byamah B Mutamba, Noeline Nakasujja
Feasibility of screening for cognitive impairment among older persons and referral by community health workers in Wakiso district, Uganda
BMC psychiatry
active learning al attempts select informative samples dataset minimize number required labels maximizing performance model current al segmentation tasks limited expansion popular classificationbased methods including entropy mcdropout etc meanwhile applications medical field simply migrations fail consider nature medical images high class imbalance high domain difference data scarcity study address challenges propose novel al framework medical image segmentation task approach introduces pseudolabelbased filter addressing excessive blank patches medical abnormalities segmentation tasks eg lesions tumors used al selection filter helps reduce resource usage allows model focus selecting informative samples sample selection propose novel query strategy combines model impact data stability employing adversarial attack furthermore harness adversarial samples generated query process enhance robustness model experimental results verify frameworks effectiveness various stateoftheart methods proposed method needs less 14 annotated patches 3d brain mri multiple sclerosis ms segmentation tasks 20 lowgrade glioma lgg tumor segmentation achieve competitive results full supervision promising outcomes improve performance alleviate time burden associated expert annotation thereby facilitating advancements field medical image segmentation code available httpsgithubcomhelenma9998adversarialactivelearning