Who should we trust? A text-free inference framework for medical image segmentation

This publication has 15 references indexed in Scilit:

• A Recent Survey of Vision Transformers for Medical Image Segmentation
  IEEE Access, 2025
• STPNet: Scale-Aware Text Prompt Network for Medical Image Segmentation
  IEEE Transactions on Image Processing, 2025
• Bridging multi-level gaps: Bidirectional reciprocal cycle framework for text-guided label-efficient segmentation in echocardiography
  Medical Image Analysis, 2025
• A comprehensive review of deep learning for medical image segmentation
  Neurocomputing, 2024
• CSWin-UNet: Transformer UNet with cross-shaped windows for medical image segmentation
  Information Fusion, 2024
• Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
  Neural Networks, 2024
• Deep learning for medical image segmentation: State-of-the-art advancements and challenges
  Informatics in Medicine Unlocked, 2024
• A visual–language foundation model for pathology image analysis using medical Twitter
  Nature Medicine, 2023
• MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset
  medRxiv, 2020
• A Multi-Organ Nucleus Segmentation Challenge
  IEEE Transactions on Medical Imaging, 2019