2 citations · 2 across the 4 of their papers we have counts for
7 papers
Resource-Efficient Glioma Segmentation on Sub-Saharan MRI
Freedmore Sidume, Oumayma Soula, Joseph Muthui Wacira +10
Gliomas are the most prevalent type of primary brain tumors, and their accurate segmentation from MRI is critical for diagnosis, treatment planning, and longitudinal monitoring. Ho…
Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Toufiq Musah, Chinasa Kalaiwo, Maimoona Akram +7
Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In…
SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
Ilerioluwakiiye Abolade, Emmanuel Idoko, Solomon Odelola +6
Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols r…
Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Toufiq Musah, Chantelle Amoako-Atta, John Amankwaah Otu +21
Brain tumors are among the deadliest cancers worldwide, with particularly devastating impact in Sub-Saharan Africa (SSA) where limited access to medical imaging infrastructure and…
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
Jeremiah Fadugba, Isabel Lieberman, Olabode Ajayi +6
Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologis…
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa
Rancy Chepchirchir, Jill Sunday, Raymond Confidence +5
In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for c…