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Found 3 papers, 1 papers with code
Twin Co-Adaptive Dialogue for Progressive Image Generation
Modern text-to-image generation systems have enabled the creation of remarkably realistic and high-quality visuals, yet they often falter when handling the inherent ambiguities in user prompts.
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
Pulmonary nodules are critical indicators for the early diagnosis of lung cancer, making their detection essential for timely treatment.
MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images
In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique. Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly. Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies. By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications.
