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Found 7 papers, 6 papers with code
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling
This demonstrates ORacle's potential to significantly enhance the scalability and affordability of OR domain modeling and opens a pathway for future advancements in surgical data science.
Ranked #1 on
Scene Graph Generation
on 4D-OR
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance
Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting
However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods.
Ranked #1 on
Structured Report Generation
on Rad-ReStruct
Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making.
LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms
The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR understanding.
Ranked #3 on
Scene Graph Generation
on 4D-OR
Unsupervised pre-training of graph transformers on patient population graphs
We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.
Ranked #1 on
Length-of-Stay prediction
on MIMIC-III
