Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training.
IEEE J Biomed Health Inform
; 28(7): 3997-4009, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38954559
ABSTRACT
Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
/
Tipos_de_cancer
/
Colon_e_reto
/
Prostata
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
/
Reto
/
Imageamento por Ressonância Magnética
/
Artefatos
Limite:
Humans
/
Male
Idioma:
En
Revista:
IEEE J Biomed Health Inform
Ano de publicação:
2024
Tipo de documento:
Article