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Found 4 papers, 1 papers with code
Increasing SAM Zero-Shot Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
This study develops and evaluates a novel multimodal medical image zero-shot segmentation algorithm named Text-Visual-Prompt SAM (TV-SAM) without any manual annotations.
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations.
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.
Self-supervised Character-to-Character Distillation for Text Recognition
Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution.
