Tze Chang Liu
A Case Study of the Adaptive Learning Platform in a Taiwanese Elementary School: Precision Education from Teachers' Perspectives
Education and information technologies
success deep learning methodologies hinges upon availability meticulously labeled extensive datasets however dealing medical images annotation process abundant training data often necessitates involvement experienced radiologists thereby consuming limited time resources order alleviate burden fewshot learning approaches developed manage achieve competitive performance levels several labeled images nevertheless crucial yet previously overlooked problem fewshot learning selection template images annotation learning affects final performance study propose novel template choosing policy tecp aims identify select worthy images annotation particularly within context multiple fewshot medical tasks including landmark detection anatomy detection anatomy segmentation tecp composed four integral components 1 selfsupervised training entails training preexisting deep model extract salient features radiological images 2 alternative proposals localizing informative regions within images 3 representative score estimation involves evaluation identification representative samples templates 4 ranking rank candidates select one highest representative score efficacy tecp approach demonstrated series comprehensive experiments conducted multiple public datasets across three medical tasks utilization tecp yields noticeable improvements model performance