Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.

ABSTRACT:

Background

We reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artificial differences in co-occurrence probability (CoOCP) in training datasets, that is, focusing CNN attention on soft tissues while ignoring bones. However, the effectiveness of this attention-based data augmentation has not been confirmed by explainable techniques. Furthermore, compared to reasonable ground truths, the feasibility of tumor segmentation in clinical kilovolt (kV) X-ray fluoroscopic (XF) images has not been confirmed.

Purpose

The first aim of this paper was to present evidence that the proposed method provides an exp

SUBMITTER: Terunuma T 

PROVIDER: S-EPMC10100026 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature