Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous, it is challenging to train general-purpose discriminative cancer detection models, with data availability and disease complexity often cited as a limiting factor. Unsupervised anomaly detection models have been suggested as a putative solution. These models learn a healthy representation of tissue and detect cancer by predicting deviations from the healthy norm, which requires models capable of accurately learning long-range interactions between organs and their imaging patterns with high levels of expressivity. Such characteristics are suitably satisfied by transformers, which have been shown to generate state-of-the-art results in unsupervised anomaly detection by training on normal data. This work expands upon such approaches by introducing multi-modal conditioning of the transformer via cross-attention i.e. supplying anatomical reference from paired CT. Using 294 whole-body PET/CT samples, we show that our anomaly detection method is robust and capable of achieving accurate cancer localization results even in cases where normal training data is unavailable. In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach with limited training data. Lastly, we propose to combine model uncertainty with a new kernel density estimation approach, and show that it provides clinically and statistically significant improvements when compared to the classic residual-based anomaly maps. Overall, a superior performance is demonstrated against leading state-of-the-art alternatives, drawing attention to the potential of these approaches.

翻译：癌症是一种高度异质性的疾病，几乎可发生于人体任何部位。18F-氟代脱氧葡萄糖成像模态因其高灵敏度及能清晰显示代谢活动模式，常被用于癌症检测。然而，由于癌症的高度异质性，训练通用型判别式癌症检测模型颇具挑战，数据可用性与疾病复杂性常被视为限制因素。无监督异常检测模型被提出作为潜在解决方案。这类模型学习正常组织表征，并通过预测偏离健康模式的偏差来检测癌症，这要求模型能够以高表达力精准学习器官间长程交互及其成像模式。Transformer的特性恰好满足此类需求，其在正常数据上训练时已在无监督异常检测中展现出最先进性能。本研究通过交叉注意力机制（即从配对的CT中提供解剖参考）引入Transformer的多模态条件化，扩展了此类方法。利用294例全身PET/CT样本，我们证明该异常检测方法具有鲁棒性，即使在缺乏正常训练数据的情况下也能实现精准的癌症定位效果。此外，我们在样本外数据上验证了该方法有效性，展示了其在有限训练数据下的泛化能力。最后，我们提出将模型不确定性与新型核密度估计方法相结合，结果显示与经典残差异常图相比，该方法可带来临床与统计学意义的显著改进。总体而言，本方法相较于当前领先的替代方案展现出更优性能，凸显了此类方法的潜力。