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Found 6 papers, 5 papers with code
Towards Building Multilingual Language Model for Medicine
In this paper, we aim to develop an open-source, multilingual language model for medicine, that the benefits a wider, linguistically diverse audience from different regions.
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-LLaMA: Towards Building Open-source Language Models for Medicine
Our contributions are threefold: (i) we systematically investigate the process of adapting a general-purpose foundation language model towards medical domain, this involves data-centric knowledge injection through the integration of 4. 8M biomedical academic papers and 30K medical textbooks, as well as comprehensive fine-tuning for alignment with domain-specific instructions; (ii) we contribute a large-scale, comprehensive dataset for instruction tuning.
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
GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS
In this paper, we optimize the semantic segmentation model in order to fully utilize the limited memory and the supported operators on the CNN accelerator chips, and at the same time reduce the CPU load of the CNN model to zero.
