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Adaptive Feature Selection for Model Observers: Reducing Reliance on Prior Knowledge for Task-based Assessment

Lookup NU author(s): Dr Anando SenORCiD


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Mathematical model observers that are applicable for clinically realistic tasks are of particular interest for task-based assessments. We propose an efficient search-capable model observer that can operate without explicit background knowledge. In place of existing scanning-observer frameworks that invoke background subtraction, the model generates adaptive binary discriminants from feature data containing implicit background information. Initial validation of the model against human-observer data from a PET localization ROC (LROC) study is presented.

Publication metadata

Author(s): Gifford HC, Sen A, Azencott R

Editor(s): King M; Glick S; Mueller K

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

Year of Conference: 2015

Pages: 45-48

Online publication date: 01/06/2015

Acceptance date: 19/02/2015

Publisher: Fully3D