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Found 7 papers, 2 papers with code
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
A radiomics approach to analyze cardiac alterations in hypertension
Hypertension is a medical condition that is well-established as a risk factor for many major diseases.
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions
In particular, we prove that in the scaling limits, the empirical measure of the Langevin particles converges to the law of a reflected It\^{o} diffusion-drift process.
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows.
