Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation
This work addresses medical image segmentation for healthcare applications, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackled the problem of skin lesion segmentation by proposing a hybrid Transformer-CNN architecture to combine global dependencies from transformers with low-level spatial details from CNNs, resulting in improved segmentation performance as evaluated through experiments.
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.