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Found 5 papers, 4 papers with code
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts
Our main contributions are three folds: (i) on data construction, we combine multiple knowledge sources to construct a multi-modal medical knowledge tree; Then we build up a large-scale segmentation dataset for training, by collecting over 11K 3D medical image scans from 31 segmentation datasets with careful standardization on both visual scans and label space; (ii) on model training, we formulate a universal segmentation model, that can be prompted by inputting medical terminologies in text form.
Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis
Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public.
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information.
Ranked #1 on
Medical Visual Question Answering
on PMC-VQA
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP.
Ranked #3 on
Medical Visual Question Answering
on PMC-VQA
K-Space Transformer for Undersampled MRI Reconstruction
This paper considers the problem of undersampled MRI reconstruction.
