2 citations · 3 across the 6 of their papers we have counts for
7 papers
VL-OrdinalFormer: Vision Language Guided Ordinal Transformers for Interpretable Knee Osteoarthritis Grading
Zahid Ullah, Jihie Kim
Knee osteoarthritis (KOA) is a leading cause of disability worldwide, and accurate severity assessment using the Kellgren Lawrence (KL) grading system is critical for clinical deci…
Unified Review and Benchmark of Deep Segmentation Architectures for Cardiac Ultrasound on CAMUS
Zahid Ullah, Muhammad Hilal, Eunsoo Lee +2
Several review papers summarize cardiac imaging and DL advances, few works connect this overview to a unified and reproducible experimental benchmark. In this study, we combine a f…
Systematic Integration of Attention Modules into CNNs for Accurate and Generalizable Medical Image Diagnosis
Zahid Ullah, Minki Hong, Tahir Mahmood +1
Deep learning has become a powerful tool for medical image analysis; however, conventional Convolutional Neural Networks (CNNs) often fail to capture the fine-grained and complex f…
Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification
Zahid Ullah, Dragan Pamucar, Jihie Kim
Magnetic Resonance Imaging (MRI) is widely recognized as the most reliable tool for detecting tumors due to its capability to produce detailed images that reveal their presence. Ho…
Exploring Kolmogorov-Arnold Network Expansions in Vision Transformers for Mitigating Catastrophic Forgetting in Continual Learning
Zahid Ullah, Jihie Kim
Continual learning (CL), the ability of a model to learn new tasks without forgetting previously acquired knowledge, remains a critical challenge in artificial intelligence, partic…
Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling fra…
DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks
Zahid Ullah, Jihie Kim
Deep learning-based cardiac segmentation has seen significant advancements over the years. Many studies have tackled the challenge of anatomically incorrect segmentation prediction…