6 citations · 6 across the 8 of their papers we have counts for
9 papers · 1 filter
HDDPM: Heteroscedastic Denoising Diffusion Probabilistic Model for Quantitative Low-Count Brain PET Recovery
Raymond Confidence, Udunna C. Anazodo
Positron emission tomography (PET) seeks to balance diagnostic quality with ra-diation dose. Low-count PET noise is non-Gaussian, non-stationary, and spatial-ly dependent. It scale…
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Prabin Bohara, Pralhad Kumar Shrestha, Arpan Rai +8
Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging protocols, diverse imaging data, ex…
Domain-Adaptive Transformer for Data-Efficient Glioma Segmentation in Sub-Saharan MRI
Ilerioluwakiiye Abolade, Aniekan Udo, Augustine Ojo +5
Glioma segmentation is critical for diagnosis and treatment planning, yet remains challenging in Sub-Saharan Africa due to limited MRI infrastructure and heterogeneous acquisition…
How We Won BraTS-SSA 2025: Brain Tumor Segmentation in the Sub-Saharan African Population Using Segmentation-Aware Data Augmentation and Model Ensembling
Claudia Takyi Ankomah, Livingstone Eli Ayivor, Ireneaus Nyame +5
Brain tumors, particularly gliomas, pose significant chall-enges due to their complex growth patterns, infiltrative nature, and the variability in brain structure across individual…
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…
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…
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations
Mohannad Barakat, Noha Magdy, Jjuuko George William +4
Gliomas, the most prevalent primary brain tumors, require precise segmentation for diagnosis and treatment planning. However, this task poses significant challenges, particularly i…