Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.

ABSTRACT:

Purpose

Deep convolutional neural networks (CNN) are promising for automatic classification of dopamine transporter (DAT)-SPECT images. Reporting the certainty of CNN-based decisions is highly desired to flag cases that might be misclassified and, therefore, require particularly careful inspection by the user. The aim of the current study was to design and validate a CNN-based system for the identification of uncertain cases.

Methods

A network ensemble (NE) combining five CNNs was trained for binary classification of [¹²³I]FP-CIT DAT-SPECT images as "normal" or "neurodegeneration-typical reduction" with high accuracy (NE for classification, NEfC). An uncertainty detection module (UDM) was obtained by combining two additional NE, one trained for detection of "reduc

SUBMITTER: Budenkotte T 

PROVIDER: S-EPMC10957699 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature