M.S. Electrical and Computer Engineering
Electrical and Computer Engineering
Henry M. Rowan College of Engineering
National Science Foundation
Nidhal C. Bouaynaya, Ph.D.
Committee Member 1
Ghulam Rasool, Ph.D.
Committee Member 2
Ravi Ramachandran, Ph.D.
Artificial intelligence, Computer vision, Machine learning, Medical AI, Transformers
Computer vision in medicine; Diagnostic imaging
Bioimaging and Biomedical Optics | Electrical and Computer Engineering
The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the study investigates the performance of vision transformers (ViTs), which have shown potential for outperforming CNNs in image classification tasks. The Scopeformer, a new end-to-end architecture that combines the unique strengths of both CNNs and ViTs, is proposed to improve upon their individual performance. The study contributes to the conversation about effective approaches for tackling challenging computer vision tasks in medical imaging.
Barhoumi, Yassine, "Efficient Scopeformer: Towards Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection using Hybrid Convolution and Vision Transformer Networks" (2023). Theses and Dissertations. 3086.