Medi-care
Visual Question Answering for Medical Imaging.
Medi-Care utilizes LLaVA-Med base weights for precise visual question answering in medical images, alongside the LLaMA model for accurate answer generation. This combination enables efficient and accurate diagnostic support, enhancing healthcare outcomes.
A VQA model used in medical imaging typically comprises an image encoder to process visual input into feature vectors, often using CNNs or pre-trained models. A question encoder handles textual input and converts questions into feature representations, commonly using RNNs or transformers. A multimodal fusion component integrates the image and question embeddings, using techniques like concatenation, element-wise multiplication, or attention mechanisms. The fused representation is then processed by a classifier or sequence-to-sequence model to generate answers to the questions.
More information and code on our repository.