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Found 9 papers, 0 papers with code
Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging
On imaging, it is difficult to differentiate LMS from, for example, degenerated leiomyoma (LM), a prevalent but benign condition.
Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization
Purpose: This study evaluated the out-of-domain performance and generalization capabilities of automated medical image segmentation models, with a particular focus on adaptation to new image acquisitions and disease type.
AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines
Methods for automatically flag poor performing-predictions are essential for safely implementing machine learning workflows into clinical practice and for identifying difficult cases during model training.
Modeling Vascular Branching Alterations in Polycystic Kidney Disease
For instance, in polycystic kidney disease (PKD), drastic cyst development may lead to a significant alteration of the vascular geometry (or vascular changes may be a preceding event).
Quantifying and Visualizing Vascular Branching Geometry with Micro-CT: Normalization of Intra- and Inter-Specimen Variations
Micro-CT images of the renal arteries of intact rat kidneys, which had their vasculature injected with the contrast agent polymer Microfil, were characterized.
Shape Aware Automatic Region-of-Interest Subdivisions
The resulting subdivisions can therefore either not relate well to the actual shape or property of the region being studied (i. e., gridding methods), or be time consuming and based on user subjectivity (i. e., manual methods).
Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning
The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions.
Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification
The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning
Significance: Predicting 1p/19q status noninvasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.
