基于磁共振成像 (MRI) 的深度神经网络 (DNN) 已被广泛开发用于执行前列腺癌 (PCa) 分类。然而，在现实世界的临床情况中，前列腺 MRI 很容易受到直肠伪影的影响，已发现这会导致不正确的 PCa 分类。现有的基于DNN的方法通常没有考虑直肠伪影对PCa分类的干扰，也没有设计具体的策略来解决这个问题。在本研究中，我们提出了一种新颖的利用专有对抗样本（TPAS）策略进行针对性对抗训练，以保护 PCa 分类模型免受直肠伪影的影响。具体来说，基于临床先验知识，我们生成了具有直肠伪影模式对抗噪声的专有对抗样本，这可能会严重误导通过普通训练策略优化的 PCa 分类模型。然后，我们共同利用生成的专有对抗样本和原始样本来训练模型。为了证明我们策略的有效性，我们对多个 PCa 分类模型进行了分析实验。与普通训练策略相比，TPAS可以有效提高患者、切片和病灶级别的单参数和多参数PCa分类，并为最新的先进模型带来实质性收益。总之，TPAS 策略可以被认为是减轻直肠伪影对 PCa 分类深度学习模型影响的一种有价值的方法。

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.