38 citations · 38 across the 4 of their papers we have counts for
5 papers
MORPHA: Morphology-Constrained Training and the Limits of Cross-Acquisition Transfer in Low-Resource Malaria Microscopy
Favour Okechukwu Igwezeke, Chikodili Helen Ugwuishiwu, Joseph Uzochukwu Emesiani +4
In low-resource malaria microscopy, a model trained on one smear preparation routinely meets images from another, and how well morphology-based constraints transfer across this acq…
Optimizing the nnU-Net model for brain tumor (Glioma) segmentation Using a BraTS Sub-Saharan Africa (SSA) dataset
Chukwuemeka Arua Kalu, Adaobi Chiazor Emegoakor, Fortune Okafor +3
Medical image segmentation is a critical achievement in modern medical science, developed over decades of research. It allows for the exact delineation of anatomical and pathologic…
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…
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…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi +48
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 ye…