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Found 5 papers, 0 papers with code
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.
