Predicting traits from images lacking visual cues is challenging, as algorithms are designed to capture visually correlated ground truth. This problem is critical in biomedical sciences, and their solution can improve the efficacy of non-invasive methods. For example, a recent challenge of predicting MGMT methylation status from MRI images is critical for treatment decisions of glioma patients. Using less robust models poses a significant risk in these critical scenarios and underscores the urgency of addressing this issue. Despite numerous efforts, contemporary models exhibit suboptimal performance, and underlying reasons for this limitation remain elusive. In this study, we demystify the complexity of MGMT status prediction through a comprehensive exploration by performing benchmarks of existing models adjoining transfer learning. Their architectures were further dissected by observing gradient flow across layers. Additionally, a feature selection strategy was applied to improve model interpretability. Our finding highlighted that current models are unlearnable and may require new architectures to explore applications in the real world. We believe our study will draw immediate attention and catalyse advancements in predictive modelling with non-visible cues.

翻译：从缺乏视觉线索的图像中预测特征具有挑战性，因为算法旨在捕捉与视觉相关的真实情况。这一问题在生物医学科学中至关重要，其解决方案可提升非侵入性方法的效能。例如，近期从MRI图像预测MGMT甲基化状态的挑战对于胶质瘤患者的治疗决策至关重要。在这些关键场景中使用稳健性不足的模型会带来重大风险，并凸显了解决此问题的紧迫性。尽管已有诸多尝试，现有模型仍表现出次优性能，且导致此局限的根本原因尚不明确。在本研究中，我们通过对现有模型进行迁移学习结合的基准测试，全面探索了MGMT状态预测的复杂性。通过观察各层梯度流进一步剖析了模型架构。此外，应用特征选择策略以提升模型可解释性。我们的发现强调，当前模型具有不可学习性，可能需要新架构来探索现实世界中的应用。我们相信本研究将引起学界即时关注，并推动基于非可见线索的预测建模领域的发展。