6 papers
Confidence-Weighted Semi-Supervised Learning for Skin Lesion Segmentation Using Hybrid CNN-Transformer Networks
Saqib Qamar
Automated skin lesion segmentation through dermoscopic analysis is essential for early skin cancer detection, yet remains challenging due to limited annotated training data. We pre…
UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
Saqib Qamar, Mohd Fazil, Parvez Ahmad +2
Medical image segmentation plays an important role in various clinical applications; however, existing deep learning models face trade-offs between efficiency and accuracy. Convolu…
ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation
Saqib Qamar, Syed Furqan Qadri, Roobaea Alroobaea +2
Melanoma is a malignant tumor that originates from skin cell lesions. Accurate and efficient segmentation of skin lesions is essential for quantitative analysis but remains a chall…
Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning
Saqib Qamar, Abu Imran Baba, Stéphane Verger +1
Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial fo…
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
Saqib Qamar, Parvez Ahmad, Linlin Shen
The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel MRI images. Quantitative ana…
Context Aware 3D UNet for Brain Tumor Segmentation
Parvez Ahmad, Saqib Qamar, Linlin Shen +1
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medica…