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Found 16 papers, 10 papers with code
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data.
GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI
Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge.
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github. com/uni-medical/IMIS-Bench.
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
We will fully release SlideChat, SlideInstruction and SlideBench as open-source resources to facilitate research and development in computational pathology.
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
The field of computer vision applied to videos of minimally invasive surgery is ever-growing.
A Survey for Large Language Models in Biomedicine
To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs.
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored.
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields.
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Supervised Finetuning (SFT) serves as an effective way to adapt such foundation models for task-specific downstream tasks but at the cost of degrading the general knowledge previously stored in the original foundation model. To address this, we propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model at minimal additional training expense for an extra gating network.
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding
The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding.
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
Importantly, all images in this benchmark are sourced from authentic medical scenarios, ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs.
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications.
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumetric medical images.
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.
