Search Results for author:
Found 4 papers, 1 papers with code
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Deep learning techniques, despite their potential, often suffer from a lack of reproducibility and generalizability, impeding their clinical adoption.
Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls
We investigate three methodological pitfalls: (1) violation of independence assumption, (2) model evaluation with an inappropriate performance indicator or baseline for comparison, and (3) batch effect.
Spectral image clustering on dual-energy CT scans using functional regression mixtures
In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves.
Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? -- A Case Study using Dual Energy CT Data
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i. e. binary code which can be executed but is difficult to be understood by a potential attacker.
