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Enhancing Precision in Rectal Cancer Radiotherapy: Localized Fine-Tuning of Deep-learning based Auto-segmentation (DLAS) Model for Clinical Target Volume and Organs-at-risk

2024-02-08

Abstract excerpt

<h4>Background: </h4> and Purpose Various deep learning auto-segmentation (DLAS) models have been proposed, some of which commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality an...

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Literature Corpus work
5016e0ee-1f5f-5c6f-bdcc-a5f93a2d2492
DOI
10.21203/rs.3.rs-3933902/v1
Open publication

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Enhancing Precision in Rectal Cancer Radiotherapy: Localized Fine-Tuning of Deep-learning based Auto-segmentation (DLAS) Model for Clinical Target Volume and Organs-at-riskDOI 10.21203/rs.3.rs-3933902/v1
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