Search Results for author:
Found 7 papers, 2 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.
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint.
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.
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.
FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation
The core module in FADO consists of a dual-level feedback strategy selector and a double control reader.
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images.
