Naira Ikram, Laura B Lewandowski, Melissa H Watt, Christiaan Scott
Barriers and facilitators to medical care retention for pediatric systemic lupus erythematosus in South Africa: a qualitative study
Pediatric rheumatology online journal
automated karyotyping great importance cytogenetic research speeds process cytogeneticists incorporating aidriven automated segmentation classification techniques existing frameworks confront two primary issues firstly necessity instancelevel data annotation either detection bounding boxes semantic masks training secondly poor robustness particularly confronted domain shifts work first propose accurate segmentation framework namely karyoxpert framework leverages strengths morphology algorithms deep learning models allowing efficient training breaks limit acquirement manually labeled groundtruth mask annotations additionally present accurate classification model based metric learning designed overcome challenges posed interclass similarity batch effects framework exhibits stateoftheart performance exceptional robustness chromosome segmentation classification proposed karyoxpert framework showcases capacity instancelevel chromosome segmentation even absence annotated data offering novel insights research automated chromosome segmentation proposed method successfully deployed support clinical karyotype diagnosis