RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
CoRR(2024)
摘要
Deep learning techniques, despite their potential, often suffer from a lack
of reproducibility and generalizability, impeding their clinical adoption.
Image segmentation is one of the critical tasks in medical image analysis, in
which one or several regions/volumes of interest should be annotated. This
paper introduces the RIDGE checklist, a framework for assessing the
Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of
deep learning-based medical image segmentation models. The checklist serves as
a guide for researchers to enhance the quality and transparency of their work,
ensuring that segmentation models are not only scientifically sound but also
clinically relevant.
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