Transformer Based Deep

Transformer-based deep learning is revolutionizing various fields by leveraging the self-attention mechanism to capture long-range dependencies in data, surpassing traditional methods in many applications. Current research focuses on adapting transformer architectures like Vision Transformers, Swin Transformers, and GPT models to diverse tasks, including medical image analysis (e.g., skin disease classification, glioblastoma survival prediction), spatial transcriptomics, and time series prediction (e.g., stock prices, COVID-19 growth). This approach offers improved accuracy and interpretability across numerous domains, leading to significant advancements in areas like healthcare, retail analytics, and materials science.