Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment· Agent-Guided Relational Concept Discovery
Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spectra collected from resected tissue samples, while they must operate on noisy, unlabeled data acquired directly during surgery. In addition, the black-box nature of deep learning models makes it difficult to understand and systematically improve their behavior. Concept-based learning offers a promising way to address these challenges by mapping raw measurements to human-understandable concepts. However, supervised concept-based approaches rely on concept annotations, which ar
通过代理引导的关系概念发现提升手术边缘评估的可解释性。
- 核心方法
- 提出了一种代理引导的关系概念发现方法，该方法能够将原始测量数据映射到人类可理解的概念，而不依赖于有标签的概念注释。
- 适合谁读
- 研究者、工程师、临床医生
- 要解决的问题
- 解决深度学习模型在手术室条件下评估手术边缘时的泛化问题和黑盒性质，提高临床应用的可能性。
- 关键实验
- 在REIMS数据集上进行了实验，展示了该方法在手术边缘评估任务中的有效性；与传统深度学习模型和监督概念学习方法进行了对比。
- 主要贡献
- 提供了一种新的方法来提升深度学习模型的可解释性和泛化能力，使得模型更适合在手术环境中使用。
- 意义与局限
- 该方法有助于解决临床应用中的泛化问题，提高模型的可解释性，从而增加医生对模型的信任度；但目前仅在特定数据集上验证，需进一步在实际手术场景中测试。