10 papers
Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
Fares Bougourzi, Fadi Dornaika, Abdenour Hadid
Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approach…
MambaCAFU: Hybrid Multi-Scale and Multi-Attention Model with Mamba-Based Fusion for Medical Image Segmentation
T-Mai Bui, Fares Bougourzi, Fadi Dornaika +1
In recent years, deep learning has shown near-expert performance in segmenting complex medical tissues and tumors. However, existing models are often task-specific, with performanc…
Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
Bouthaina Slika, Fadi Dornaika, Fares Bougourzi +1
Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imagin…
Recent Advances in Medical Imaging Segmentation: A Survey
Fares Bougourzi, Abdenour Hadid
Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of…
Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging
Fadi Abdeladhim Zidi, Abdelkrim Ouafi, Fares Bougourzi +2
As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climat…
Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach
Mohamed Fadhlallah Guerri, Cosimo Distante, Paolo Spagnolo +2
During the process of classifying Hyperspectral Image (HSI), every pixel sample is categorized under a land-cover type. CNN-based techniques for HSI classification have notably adv…