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Found 5 papers, 1 papers with code
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.
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.
High Resolution Medical Image Analysis with Spatial Partitioning
It is infeasible to train CNN models directly on such high resolution images, because neural activations of a single image do not fit in the memory of a single GPU/TPU, and naive data and model parallelism approaches do not work.
Interactive segmentation of medical images through fully convolutional neural networks
Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.
