Search Results for author:
Found 6 papers, 1 papers with code
MINT: A wrapper to make multi-modal and multi-image AI models interactive
In this paper we tackle a more subtle challenge: doctors take a targeted medical history to obtain only the most pertinent pieces of information; how do we enable AI to do the same?
Improved distinct bone segmentation in upper-body CT through multi-resolution networks
The approach thus improves the accuracy and efficiency of distinct bone segmentation from upper-body CT.
Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images
Results: Our methods are evaluated on an in-house dataset of 16 upper-body CT scans with a resolution of \SI{2}{\milli\meter} per dimension.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images
Employing a hyperparameter search, we find a family of convolutional neural networks achieving similar results as with the manual classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for dwarf irregulars.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis.
