Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency. Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model. Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives. Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g. variations in size and appearance, while discarding misleading features such as staining differences. Our results on breast lymph node tissue show significantly improved generalization in the detection of tumorous tissue, with best average AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. This result is a starting point towards building interpretable multi-task architectures that are robust to data heterogeneity. Our code is available at https://github.com/maragraziani/multitask_adversarial

翻译：将卷积神经网络应用于初级诊断的日常流程，不仅需要接近完美的精确度，还需要对数据采集偏移具有充分的泛化能力与透明性。现有CNN模型如同黑箱，无法向医生保证模型使用了重要的诊断特征。本文基于多任务学习、领域对抗训练与基于概念的模型可解释性等现有成功技术，致力于在训练目标中引入诊断因子。我们证明，通过端到端学习基于不确定性的多任务损失与对抗损失加权组合，所提出的架构能够聚焦于核密度与多形性（如大小与形态变化）等病理特征，同时摒弃染色差异等误导性特征。在乳腺淋巴结组织上的结果表明，肿瘤组织检测的泛化性能显著提升，平均AUC最佳达0.89（±0.01），而基线AUC为0.86（±0.005）。通过应用线性探针中间表征的可解释性技术，我们还验证了所提出的CNN架构学习了核密度等可解释的病理特征，证实了该模型透明性的提升。这一结果为构建对数据异质性鲁棒的可解释多任务架构奠定了基础。我们的代码发布在 https://github.com/maragraziani/multitask_adversarial