Biomarker Localization From Deep Learning Regression Networks.

ABSTRACT: Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression net

SUBMITTER: Cano-Espinosa C 

PROVIDER: S-EPMC7307703 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature