Jill Selesa Sunday<\/p>\n\n\n\n
In resource-constrained Sub-Saharan Africa (SSA), the utilization of lower-quality MRI technology raises questions about the applicability of machine learning (ML) methods for clinical tasks. The challenges associated with inadequate image contrast and resolution in SSA-acquired brain MRI scans demand advanced pre-processing methods to improve the image quality before employing ML techniques for tasks like tumor segmentation and classification.<\/p>\n\n\n\n
Addressing this, the aim of the BRaTS-Africa 2023 challenge was to provide a platform for research and to come up with a generalizable and robust deep learning-based brain tumor segmentation method tailored for the SSA population.<\/p>\n\n\n\n
This study presents a threefold approach. First, the impact of domain shift from SSA training data on model efficacy is highlighted, revealing a discernible influence despite the small dataset size. Secondly, a comparative analysis of 3D and 2D full-resolution models using the nnU-Net version 2 framework indicates the superiority of a 2D model trained for 300 epochs achieving a five-fold cross-validation score of 0.9278. Finally, with respect to the performance gap observed in SSA validation as opposed to GLI validation, two strategies are proposed: fine-tuning SSA cases using the GLI+SSA best pre-trained 2D full resolution model at 300 epochs, and introducing a novel neural style transfer-based data augmentation technique for the SSA cases. The fusion of these strategies significantly improves SSA validation results within computational limitations.<\/p>\n\n\n\n
This investigation underscores the potential of notable performance improvements in enhancing brain tumor prediction within SSA\u2019s unique healthcare setting.<\/p>\n","protected":false},"excerpt":{"rendered":"
Jill Selesa Sunday ABSTRACT In resource-constrained Sub-Saharan Africa (SSA), the utilization of lower-quality MRI technology raises questions about the applicability of machine learning (ML) methods […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":111,"menu_order":29,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-553","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/pages\/553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=553"}],"version-history":[{"count":1,"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/pages\/553\/revisions"}],"predecessor-version":[{"id":554,"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/pages\/553\/revisions\/554"}],"up":[{"embeddable":true,"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=\/wp\/v2\/pages\/111"}],"wp:attachment":[{"href":"https:\/\/pri.tum.ac.ke\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}