2024/01/10 by Siddharth Tiwari, Tiwari, Siddharth
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.05481
openalex publication_date 2024/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The segmentation of medical images is important for the improvement and creation of healthcare systems, particularly for early disease detection and treatment planning. In recent years, the use of convolutional neural networks (CNNs) and other state-of-the-art methods has greatly advanced medical image segmentation. However, CNNs have been found to struggle with learning long-range dependencies and capturing global context due to the limitations of convolution operations. In this paper, we explore the use of transformers and CNNs for medical image segmentation and propose a hybrid architecture that combines the ability of transformers to capture global dependencies with the ability of CNNs to capture low-level spatial details. We compare various architectures and configurations and conduct multiple experiments to evaluate their effectiveness.